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Freelance Data Analyst and Academic Consultants | Expertise in Applied Neuroscience and Neuroimaging

Profile Summary

With over 13 years of experience in translational human research, my expertise lies at the intersection of biomedical engineering, neuroscience, and data science. I have received advanced interdisciplinary training in these fields, equipping me with the necessary knowledge and skills to drive innovative solutions. Throughout my career, I have primarily focused on non-invasive neuroimaging techniques such as EEG/MEG and MRI/fMRI, as well as neurofeedback. One of my key strengths is my ability to conduct neuropsychiatric studies effectively. By leveraging my expertise in signal processing and machine learning, I have been able to extract valuable insights from complex datasets, delivering novel and data-driven findings. My proficiency in MATLAB allows me to efficiently analyze and interpret data, while I also possess additional experience working with Python and R. I approach my work with a creative and passionate mindset, constantly seeking innovative approaches to problem-solving. Attention to detail is a quality I hold in high regard, ensuring that every aspect of my research is meticulously executed. Furthermore, my entrepreneurial spirit drives me to explore new avenues and push the boundaries of scientific inquiry. In summary, my interdisciplinary training, extensive research experience, and proficiency in various analytical tools make me a valuable asset in the fields of biomedical engineering, neuroscience, and data science. My commitment to delivering impactful results and my drive for innovation set me apart, making me a capable and forward-thinking professional.

Subject Matter Expertise

• ★ Neuroscience
• ★ Signal Processing
• ★ Machine Learning
• ☆ Biotechnology & Bioengineering
• ☆ Electrical Engineering
• ☆ Biological Engineering
• ☆ Data Analysis
• ☆ Data Processing
• ☆ Neural Networks
• ☆ Earth & Atmospheric Science
• ☆ Mining
• ☆ Python
• ☆ Matlab
• ☆ LaTex
• ☆ Health Informatics
• ☆ Complementary & Alternative Medicine
• ☆ Biofeedback
• ☆ Research Methodology
• ☆ Human Resources
• ☆ Employee Training

Services

• Writing
• Research
• Consulting
• Data & AI

Writing Technical Writing, Newswriting

Research Gap Analysis, Gray Literature Search, Scientific and Technical Research, Systematic Literature Review, Secondary Data Collection

Consulting Healthcare Consulting, Scientific and Technical Consulting

Data & AI Statistical Analysis, Image Processing, Algorithm Design-ML, Data Visualization, Data Mining, Data Processing

Work Experience

Associate Professor

Universiy of York Europe Campus CITY College

February 2020 - Present

Lecturer

Aston University

July 2017 - December 2019

Researcher

Technische Universität Dresden

October 2016 - July 2017

Postdoctoral Researcher

Max-Planck-Institut für Kognitions- und Neurowissenschaften

October 2014 - September 2016

CEO/PI

Neurofeedback Center of Northern Greece

2013 - 2015

Research Assistant

Aristotle University of Thessaloniki

2012 - 2014

Research Assistant

University of Nicosia

2011 - 2011

Research Assistant

Aristotle University of Thessaloniki

2007 - 2010

Education

PGCert in Higher Education

Aston University

October 2017 - June 2018

PhD (Medical School)

Aristotle University of Thessaloniki

June 2010 - September 2014

MSc in Medical Informatics (Medical School)

Aristotle University of Thessaloniki

January 2007 - July 2009

BSc in Mathematics (Mathematics)

Aristotle University of Thessaloniki

October 2001 - July 2007

Certifications

• Certification details not provided.

Publications

JOURNAL ARTICLE

Christos Stergiadis, Vasiliki-Despoina Kostaridou, Simos Veloudis, Dimitrios Kazis, Manousos A. Klados (2022). A Personalized User Authentication System Based on EEG Signals . Sensors.

Manousos Klados, Christos Stergiadis, Vasiliki-Despoina Kostaridou, Simeon Veloudis, Dimitrios Kazis (2022). A Personalized User Authentication System Based on EEG Signals . Sensors.

Manousos Klados, Alexandra Anagnostopoulou, Charis Styliadis, Panagiotis Kartsidis, Evangelia Romanopoulou, Vasiliki Zilidou, Chrysi Karali, Maria Karagianni, Evangelos Paraskevopoulos, Panagiotis D. Bamidis(2021). Computerized physical and cognitive training improves the functional architecture of the brain in adults with Down syndrome: A network science EEG study . Network Neuroscience. 5. (1). p. 274--294. {MIT} Press - Journals

Manousos A. Klados, Panagiota Konstantinidi, Rosalia Dacosta-Aguayo, Vasiliki-Despoina Kostaridou, Alessandro Vinciarelli, Michalis Zervakis(2020). Automatic Recognition of Personality Profiles Using EEG Functional Connectivity during Emotional Processing . Brain Sciences. 10. (5). p. 278. {MDPI} {AG}

Automatic Recognition of Personality Profiles Using EEG Functional Connectivity During Emotional Processing @article{cfb84a684b924964ba9d61938bed536c, title = "Automatic Recognition of Personality Profiles Using EEG Functional Connectivity During Emotional Processing", abstract = "Personality is the characteristic set of an individual{\textquoteright}s behavioral and emotional patterns that evolve from biological and environmental factors. The recognition of personality profiles is crucial in making human–computer interaction (HCI) applications realistic, more focused, and user friendly. The ability to recognize personality using neuroscientific data underpins the neurobiological basis of personality. This paper aims to automatically recognize personality, combining scalp electroencephalogram (EEG) and machine learning techniques. As the resting state EEG has not so far been proven efficient for predicting personality, we used EEG recordings elicited during emotion processing. This study was based on data from the AMIGOS dataset reflecting the response of 37 healthy participants. Brain networks and graph theoretical parameters were extracted from cleaned EEG signals, while each trait score was dichotomized into low- and high-level using the k-means algorithm. A feature selection algorithm was used afterwards to reduce the feature-set size to the best 10 features to describe each trait separately. Support vector machines (SVM) were finally employed to classify each instance. Our method achieved a classification accuracy of 83.8% for extraversion, 86.5% for agreeableness, 83.8% for conscientiousness, 83.8% for neuroticism, and 73% for openness.", author = "Klados, {Manousos A.} and Panagiota Konstantinidi and Rosalia Dacosta-aguayo and Vasiliki-despoina Kostaridou and Alessandro Vinciarelli and Michalis Zervakis", note = "This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited", year = "2020", month = may, day = "3", doi = "10.3390/brainsci10050278", language = "English", volume = "10", journal = "Brain Sciences", issn = "2076-3425", publisher = "MDPI AG", number = "5", } . Brain Sciences.

Nicolina Sciaraffa, Manousos A. Klados, Gianluca Borghini, Gianluca Di Flumeri, Fabio Babiloni, Pietro Aricò(2020). Double-Step Machine Learning Based Procedure for HFOs Detection and Classification . Brain Sciences. 10. (4). p. 220. {MDPI} {AG}

Manousos Klados, Nicolina Sciaraffa, Gianluca Borghini, GIANLUCA DI FLUMERI, Fabio Babiloni, Pietro Aricò (2020). Double-Step Machine Learning Based Procedure for HFOs Detection and Classification . Brain Sciences.

Tomas Ros, Stefanie Enriquez-Geppert, Vadim Zotev, Kymberly D Young, Guilherme Wood, Susan Whitfield-Gabrieli, Feng Wan, Patrik Vuilleumier, François Vialatte, Dimitri Van De Ville, et al.(2020). Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist) . Brain. 143. (6). p. 1674--1685. Oxford University Press ({OUP})

Functional connectivity analysis of cerebellum using spatially constrained spectral clustering @article{0585cf6770204cab9b7e882cd4a341ef, title = "Functional connectivity analysis of cerebellum using spatially constrained spectral clustering", abstract = "The human cerebellum contains almost 50% of the neurons in the brain, although its volume does not exceed 10% of the total brain volume. The goal of this study is to derive the functional network of the cerebellum during the resting-state and then compare the ensuing group networks between males and females. Toward this direction, a spatially constrained version of the classic spectral clustering algorithm is proposed and then compared against conventional spectral graph theory approaches, such as spectral clustering, and N-cut, on synthetic data as well as on resting-state fMRI data obtained from the Human Connectome Project (HCP). The extracted atlas was combined with the anatomical atlas of the cerebellum resulting in a functional atlas with 46 regions of interest. As a final step, a gender-based network analysis of the cerebellum was performed using the data-driven atlas along with the concept of the minimum spanning trees. The simulation analysis results confirm the dominance of the spatially constrained spectral clustering approach in discriminating activation patterns under noisy conditions. The network analysis results reveal statistically significant differences in the optimal tree organization between males and females. In addition, the dominance of the left VI lobule in both genders supports the results reported in a previous study of ours. To our knowledge, the extracted atlas comprises the first resting-state atlas of the cerebellum based on HCP data.", keywords = "Cerebellum, gender, minimum spanning trees, resting-state fMRI, spatially constrained spectral clustering", author = "Pezoulas, {Vasileios C.} and Kostas Michalopoulos and Manousos Klados and Sifis Micheloyannis and Nikolaos Bourbakis and Michalis Zervakis", note = "{\textcopyright} 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ", year = "2019", month = jul, day = "1", doi = "10.1109/JBHI.2018.2868918", language = "English", volume = "23", pages = "1710 -- 1719", journal = "IEEE Journal of Biomedical and Health Informatics", issn = "2168-2194", publisher = "IEEE", number = "4", } . IEEE Journal of Biomedical and Health Informatics.

Vasileios C. Pezoulas, Kostas Michalopoulos, Manousos A. Klados, Sifis Micheloyannis, Nikolaos G. Bourbakis, Michalis Zervakis(2019). Functional Connectivity Analysis of Cerebellum Using Spatially Constrained Spectral Clustering . IEEE Journal of Biomedical and Health Informatics. 23. (4). p. 1710--1719. Institute of Electrical and Electronics Engineers ({IEEE})

Interactive effects of dopamine transporter genotype and aging on resting-state functional networks @article{b37cc9c65ee747bc89870caaf05c9b13, title = "Interactive effects of dopamine transporter genotype and aging on resting-state functional networks", abstract = "Aging and dopamine modulation have both been independently shown to influence the functional connectivity of brain networks during rest. Dopamine modulation is known to decline during the course of aging. Previous evidence also shows that the dopamine transporter gene (DAT1) influences the re-uptake of dopamine and the anyA9 genotype of this gene is associated with higher striatal dopamine signaling. Expanding these two lines of prior research, we investigated potential interactive effects between aging and individual variations in the DAT1 gene on the modular organization of brain acvitiy during rest. The graph-theoretic metrics of modularity, betweenness centrality and participation coefficient were assessed in 41 younger (age 20-30 years) and 37 older (age 60-75 years) adults. Age differences were only observed in the participation coefficient in carriers of the anyA9 genotype of the DAT1 gene and this effect was most prominently observed in the default mode network. Furthermore, we found that individual differences in the values of the participation coefficient correlated with individual differences in fluid intelligence and a measure of executive control in the anyA9 carriers. The correlation between participation coefficient and fluid intelligence was mainly shared with age-related differences, whereas the correlation with executive control was independent of age. These findings suggest that DAT1 genotype moderates age differences in the functional integration of brain networks as well as the relation between network characteristics and cognitive abilities.", author = "Christian Baeuchl and Hsiang-Yu Chen and Yu-Shiang Su and Dorothea H{\"a}mmerer and Klados, {Manousos A} and Shu-Chen Li", note = "{\textcopyright} 2019 Baeuchl et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.", year = "2019", month = may, day = "8", doi = "10.1371/journal.pone.0215849", language = "English", volume = "14", journal = "PLoS ONE", issn = "1932-6203", publisher = "Public Library of Science", number = "5", } . PLoS ONE.

The Impact of Math Anxiety on Working Memory @article{bab9e1366f8346fcb278b6e13245d490, title = "The Impact of Math Anxiety on Working Memory: A Cortical Activations and Cortical Functional Connectivity EEG Study", abstract = "Mathematical anxiety (MA) is defined as a feeling of tension, apprehension, or fear that interferes with mathematical performance in various daily or academic situations. Cognitive consequences of MA have been studied a lot and revealed that MA seriously affects solving the complex problem due to the corruption of working memory (WM). The corruption of WM caused by MA is well documented in behavioral level, but the involved neurophysiological processes have not been properly addressed, despite the recent attention drawn on the neural basis of MA. This is the second part of our study that intents to investigate the neurophysiological aspects of MA and its implications to WM. In the first study, we saw how MA affects the early stages of numeric stimuli processes as the WM indirectly using event-related potentials in scalp electroencephalographic (EEG) signals. This paper goes one step further to investigate the cortical activations, obtained by the multichannel EEG recordings as well as the cortical functional networks in three WM tasks with increasing difficulty. Our results indicate that the high-math anxious (HMA) group activated more areas linked with negative emotions, pain, and fear, while the low-math anxious (LMA) group activated regions related to the encoding and retrieval processes of the WM. Functional connectivity analysis also reveals that the LMAs' brain has got more structured cortical networks with increased connectivity in areas related to WM, such as the frontal cortex, while the HMAs' brain has a more diffused and unstructured network, superimposing the evidence that the structured processes of WM are corrupted.", keywords = "EEG, Mathematical anxiety, cortical functional connectivity, math anxiety, working memory", author = "Klados, {Manousos A.} and Evangelos Paraskevopoulos and Niki Pandria and Bamidis, {Panagiotis D.}", note = "{\textcopyright} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.", year = "2019", month = jan, day = "14", doi = "10.1109/ACCESS.2019.2892808", language = "English", volume = "7", pages = "15027--15039", journal = "IEEE Access", issn = "2169-3536", publisher = "IEEE", } . IEEE Access.

Manousos A. Klados, Evangelos Paraskevopoulos, Niki Pandria, Panagiotis D. Bamidis(2019). The Impact of Math Anxiety on Working Memory: A Cortical Activations and Cortical Functional Connectivity EEG Study . IEEE Access. 7. p. 15027--15039. Institute of Electrical and Electronics Engineers ({IEEE})

Manousos A. Klados, Evangelos Paraskevopoulos, Niki Pandria, Panagiotis D. Bamidis(2019). The Impact of Math Anxiety on Working Memory: A Cortical Activations and Cortical Functional Connectivity EEG Study . IEEE Access. 7. p. 15027--15039. Institute of Electrical and Electronics Engineers ({IEEE})

Manousos Klados, Liesa Ilg, Nina Alexander, Clemens Kirschbaum, Shu-Chen Li (2018). Long-term impacts of prenatal synthetic glucocorticoids exposure on functional brain correlates of cognitive monitoring in adolescence . Scientific Reports.

Long-term impacts of prenatal synthetic glucocorticoids exposure on functional brain correlates of cognitive monitoring in adolescence @article{3b884fa1fe6246b1962c0b428171c9dd, title = "Long-term impacts of prenatal synthetic glucocorticoids exposure on functional brain correlates of cognitive monitoring in adolescence", abstract = "The fetus is highly responsive to the level of glucocorticoids in the gestational environment. Perturbing glucocorticoids during fetal development could yield long-term consequences. Extending prior research about effects of prenatally exposed synthetic glucocorticoids (sGC) on brain structural development during childhood, we investigated functional brain correlates of cognitive conflict monitoring in term-born adolescents, who were prenatally exposed to sGC. Relative to the comparison group, behavioral response consistency (indexed by lower reaction time variability) and a brain correlate of conflict monitoring (the N2 event-related potential) were reduced in the sGC exposed group. Relatedly, source localization analyses showed that activations in the fronto-parietal network, most notably in the cingulate cortex and precuneus, were also attenuated in these adolescents. These regions are known to subserve conflict detection and response inhibition as well as top-down regulation of stress responses. Moreover, source activation in the anterior cingulate cortex correlated negatively with reaction time variability, whereas activation in the precuneus correlated positively with salivary cortisol reactivity to social stress in the sGC exposed group. Taken together, findings of this study indicate that prenatal exposure to sGC yields lasting impacts on the development of fronto-parietal brain functions during adolescence, affecting multiple facets of adaptive cognitive and behavioral control.", author = "Liesa Ilg and Manousos Klados and Nina Alexander and Clemens Kirschbaum and Shu-Chen Li", note = "This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Te images or other third party material in this article are included in the article{\textquoteright}s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article{\textquoteright}s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. {\textcopyright} The Author(s) 2018", year = "2018", month = may, day = "16", doi = "10.1038/s41598-018-26067-3", language = "English", volume = "8", journal = "Scientific Reports", issn = "2045-2322", publisher = "Nature Publishing Group", number = "1", } . Scientific Reports.

Math anxiety @article{287d96c3bab944d9812060adae939ba0, title = "Math anxiety: brain cortical network changes in anticipation of doing mathematics", abstract = "Following our previous work regarding the involvement of math anxiety (MA) in math-oriented tasks, this study tries to explore the differences in the cerebral networks' topology between self-reported low math-anxious (LMA) and high math-anxious (HMA) individuals, during the anticipation phase prior to a mathematical related experiment. For this reason, multichannel EEG recordings were adopted, while the solution of the inverse problem was applied in a generic head model, in order to obtain the cortical signals. The cortical networks have been computed for each band separately, using the magnitude square coherence metric. The main graph theoretical parameters, showed differences in segregation and integration in almost all EEG bands of the HMAs in comparison to LMAs, indicative of a great influence of the anticipatory anxiety prior to mathematical performance.", keywords = "cortical networks, functional connectivity, graph theory, math anxiety", author = "Klados, {Manousos A.} and Niki Pandria and Sifis Micheloyannis and Daniel Margulies and Bamidis, {Panagiotis D.}", note = "{\textcopyright} 2017, Elsevier. Licensed under the Creative Commons Attribution- NonCommercial-NoDerivatives 4.0 International.", year = "2017", month = dec, day = "1", doi = "10.1016/j.ijpsycho.2017.05.003", language = "English", volume = "122", pages = "24--31", journal = "International Journal of Psychophysiology", issn = "0167-8760", publisher = "Elsevier", } . International Journal of Psychophysiology.

A Systematic Review of Investigations into Functional Brain Connectivity Following Spinal Cord Injury @article{03032c14d52b48318dfe027f6a4b4639, title = "A Systematic Review of Investigations into Functional Brain Connectivity Following Spinal Cord Injury", abstract = "Background: Complete or incomplete spinal cord injury (SCI) results in varying degree of motor, sensory and autonomic impairment. Long-lasting, often irreversible disability results from disconnection of efferent and afferent pathways. How does this disconnection affect brain function is not so clear. Changes in brain organization and structure have been associated with SCI and have been extensively studied and reviewed. Yet, our knowledge regarding brain connectivity changes following SCI is overall lacking. Methods: In this study we conduct a systematic review of articles regarding investigations of functional brain networks following SCI, searching on PubMed, Scopus and ScienceDirect according to PRISMA-P 2015 statement standards.Results: Changes in brain connectivity have been shown even during the early stages of the chronic condition and correlate with the degree of neurological impairment. Connectivity changes appear as dynamic post-injury procedures. Sensorimotor networks of patients and healthy individuals share similar patterns but new functional interactions have been identified as unique to SCI networks.Conclusions: Large-scale, multi-modal, longitudinal studies on SCI patients are needed to understand how brain network reorganization is established and progresses through the course of the condition. The expected insight holds clinical relevance in preventing maladaptive plasticity after SCI through individualized neurorehabilitation, as well as the design of connectivity-based brain-computer interfaces and assistive technologies for SCI patients.", keywords = "brain connectivity, brain network, cortical connectivity, cortical network, maladaptive plasticity, network reorganization, sensorimotor network, spinal cord injury", author = "Alkinoos Athanasiou and Klados, {Manousos A.} and Niki Pandria and Nicolas Foroglou and Kavazidi, {Kyriaki R.} and Konstantinos Polyzoidis and Bamidis, {Panagiotis D.}", note = "Copyright {\textcopyright} 2017 Athanasiou, Klados, Pandria, Foroglou, Kavazidi, Polyzoidis and Bamidis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Funding: European Union{\textquoteright}s Horizon 2020 UNCAP project (grant number 643555). This study was conducted in the context of the project CSI:Brainwave (ClinicalTrials.gov NCT02443558; http://medphys.med.auth.gr/content/csi-brainwave) that was partially supported by the 2013 Mario Boni Research Grant, awarded by the European Section of Cervical Spine Research Society (CSRS-ES). ", year = "2017", month = oct, day = "25", doi = "10.3389/fnhum.2017.00517", language = "English", volume = "11", journal = "Frontiers in Human Neuroscience", issn = "1662-5161", publisher = "Frontiers Media S.A.", } . Frontiers in Human Neuroscience.

Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to Crystallized IQ and Gender @article{7fb3f4b1f3644860bcf73aa9207ec868, title = "Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to Crystallized IQ and Gender", abstract = "During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research investigating cerebellum's relationship with cognitive processes. In this study, the network of cerebellum was analyzed in order to investigate its overall organization in individuals with low and high crystallized Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were selected from 136 subjects in resting-state from the Human Connectome Project (HCP) database and were further separated into two IQ groups composed of 69 low-IQ and 67 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas. Thereafter, correlation matrices were constructed by computing Pearson's correlation coefficients between the average BOLD time-series for each pair of ROIs inside the cerebellum. By computing conventional graph metrics, small-world network properties were verified using the weighted clustering coefficient and the characteristic path length for estimating the trade-off between segregation and integration. In addition, a connectivity metric was computed for extracting the average cost per network. The concept of the Minimum Spanning Tree (MST) was adopted and implemented in order to avoid methodological biases in graph comparisons and retain only the strongest connections per network. Subsequently, six global and three local metrics were calculated in order to retrieve useful features concerning the characteristics of each MST. Moreover, the local metrics of degree and betweenness centrality were used to detect hubs, i.e., nodes with high importance. The computed set of metrics gave rise to extensive statistical analysis in order to examine differences between low and high-IQ groups, as well as between all possible gender-based group combinations. Our results reveal that both male and female networks have small-world properties with differences in females (especially in higher IQ females) indicative of higher neural efficiency in cerebellum. There is a trend toward the same direction in men, but without significant differences. Finally, three lobules showed maximum correlation with the median response time in low-IQ individuals, implying that there is an increased effort dedicated locally by this population in cognitive tasks.", keywords = "cerebellum, fMRI,, small-world network, minimum spanning tree, crystallized IQ, median response time", author = "Pezoulas, {Vasileios C} and Michalis Zervakis and Sifis Michelogiannis and Klados, {Manousos A}", note = "{\textcopyright} 2017 Pezoulas, Zervakis, Michelogiannis and Klados. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Funding: 6 NIH Institutes and Centers, the McDonnell Center for Systems Neuroscience at Washington University.", year = "2017", month = apr, day = "26", doi = "10.3389/fnhum.2017.00189", language = "English", volume = "11", journal = "Frontiers in Human Neuroscience", issn = "1662-5161", publisher = "Frontiers Media S.A.", } . Frontiers in Human Neuroscience.

Automated individual-level parcellation of Broca's region based on functional connectivity @article{054ca2d4b29a4db48b0b11df530d6f0e, title = "Automated individual-level parcellation of Broca's region based on functional connectivity", abstract = "Broca's region can be subdivided into its constituent areas 44 and 45 based on established differences in connectivity to superior temporal and inferior parietal regions. The current study builds on our previous work manually parcellating Broca's area on the individual-level by applying these anatomical criteria to functional connectivity data. Here we present an automated observer-independent and anatomy-informed parcellation pipeline with comparable precision to the manual labels at the individual-level. The method first extracts individualized connectivity templates of areas 44 and 45 by assigning to each surface vertex within the ventrolateral frontal cortex the partial correlation value of its functional connectivity to group-level templates of areas 44 and 45, accounting for other template connectivity patterns. To account for cross-subject variability in connectivity, the partial correlation procedure is then repeated using individual-level network templates, including individual-level connectivity from areas 44 and 45. Each node is finally labeled as area 44, 45, or neither, using a winner-take-all approach. The method also incorporates prior knowledge of anatomical location by weighting the results using spatial probability maps. The resulting area labels show a high degree of spatial overlap with the gold-standard manual labels, and group-average area maps are consistent with cytoarchitectonic probability maps of areas 44 and 45. To facilitate reproducibility and to demonstrate that the method can be applied to resting-state fMRI datasets with varying acquisition and preprocessing parameters, the labeling procedure is applied to two open-source datasets from the Human Connectome Project and the Nathan Kline Institute Rockland Sample. While the current study focuses on Broca's region, the method is adaptable to parcellate other cortical regions with distinct connectivity profiles.", keywords = "FMRI, Neuroimaging, Cortical, Parcellation, Language", author = "Estrid Jakobsen and Franziskus Liem and Klados, {Manousos A} and {\c S}eyma Bayrak and Michael Petrides and Margulies, {Daniel S}", note = " {\textcopyright} 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/). ", year = "2016", month = sep, day = "30", doi = "10.1016/j.neuroimage.2016.09.069", language = "English", volume = "170", pages = "41--53", journal = "Neuroimage", issn = "1053-8119", publisher = "Elsevier", } . NeuroImage.

A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques @article{eeb2dfe9459249fa8011c34eb63750b4, title = "A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques", abstract = "Artifact rejection techniques are used to recover the brain signals underlying artifactual electroencephalographic (EEG) segments. Although over the last few years many different artifact rejection techniques have been proposed (http://dx.doi.org/10.1109/JSEN.2011.2115236[1], http://dx.doi.org/10.1016/j.clinph.2006.09.003[2], http://dx.doi.org/10.3390/e16126553[3]), none has been established as a gold standard so far, because assessing their performance is difficult and subjective (http://dx.doi.org/10.1109/ITAB.2009.5394295[4], http://dx.doi.org/10.1016/j.bspc.2011.02.001[5], http://dx.doi.org/10.1007/978-3-540-89208-3_300. [6]). This limitation is mainly based on the fact that the underlying artifact-free brain signal is unknown, so there is no objective way to measure how close the retrieved signal is to the real one. This article solves the aforementioned problem by presenting a semi-simulated EEG dataset, where artifact-free EEG signals are manually contaminated with ocular artifacts, using a realistic head model. The significant part of this dataset is that it contains the pre-contamination EEG signals, so the brain signals underlying the EOG artifacts are known and thus the performance of every artifact rejection technique can be objectively assessed.", keywords = "EEG, EOG, Artifact Rejection", author = "Klados, {Manousos A} and Bamidis, {Panagiotis D}", note = " {\textcopyright} 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).", year = "2016", month = sep, day = "1", doi = "10.1016/j.dib.2016.06.032", language = "English", volume = "8", pages = "1004--6", journal = "Data in Brief", issn = "2352-3409", publisher = "Elsevier", } . Data in Brief.

Beta-band functional connectivity is reorganized in Mild Cognitive Impairment after combined computerized physical and cognitive training @article{6f4c050b6e3c4ee3ad99f5811d468211, title = "Beta-band functional connectivity is reorganized in Mild Cognitive Impairment after combined computerized physical and cognitive training", abstract = "Physical and cognitive idleness constitute significant risk factors for the clinical manifestation of age-related neurodegenerative diseases. In contrast, a physically and cognitively active lifestyle may restructure age-declined neuronal networks enhancing neuroplasticity. The present study, investigated the changes of brain's functional network in a group of elderly individuals at risk for dementia that were induced by a combined cognitive and physical intervention scheme. Fifty seniors meeting Petersen's criteria of Mild Cognitive Impairment were equally divided into an experimental (LLM), and an active control (AC) group. Resting state electroencephalogram (EEG) was measured before and after the intervention. Functional networks were estimated by computing the magnitude square coherence between the time series of all available cortical sources as computed by standardized low resolution brain electromagnetic tomography (sLORETA). A statistical model was used to form groups' characteristic weighted graphs. The introduced modulation was assessed by networks' density and nodes' strength. Results focused on the beta band (12–30 Hz) in which the difference of the two networks' density is maximum, indicating that the structure of the LLM cortical network changes significantly due to the intervention, in contrast to the network of AC. The node strength of LLM participants in the beta band presents a higher number of bilateral connections in the occipital, parietal, temporal and prefrontal regions after the intervention. Our results show that the combined training scheme reorganizes the beta-band functional connectivity of MCI patients. ClinicalTrials.gov Identifier: NCT02313935 https://clinicaltrials.gov/ct2/show/NCT02313935.", keywords = "Aging, Brain plasticity, Cognitive training, Electroencephalography, Graph theory, Mild cognitive impairment, Physical exercise, Resting states", author = "Klados, {Manousos A.} and Charis Styliadis and Frantzidis, {Christos A.} and Evangelos Paraskevopoulos and Bamidis, {Panagiotis D.}", note = "Copyright {\textcopyright} 2016 Klados, Styliadis, Frantzidis, Paraskevopoulos and Bamidis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Funding: European CIP-ICTPSP. 2008.1.4 Long Lasting memories (LLM) project (Project No. 238904) (http://www.longlastingmemories.eu/). LLM run from June 2009 to March 2012 under the co-ordination of the author PB and it was a partnership of 5 EU Member countries (Austria, France, Greece, Spain, and the Cyprus).", year = "2016", month = feb, day = "29", doi = "10.3389/fnins.2016.00055", language = "English", volume = "10", journal = "Frontiers in Human Neuroscience", issn = "1662-5161", publisher = "Frontiers Media S.A.", number = "FEB", } . Frontiers in Human Neuroscience.

Klados, M.A., Styliadis, C., Frantzidis, C.A., Paraskevopoulos, E., Bamidis, P.D.(2016). Beta-band functional connectivity is reorganized in Mild Cognitive Impairment after combined computerized physical and cognitive training . Frontiers in Neuroscience. 10. (FEB).

Jakobsen, E., Liem, F., Klados, M.A., Bayrak, T., Petrides, M., Margulies, D.S.(2016). Automated individual-level parcellation of Broca's region based on functional connectivity . NeuroImage.

ERP measures of math anxiety @article{4238d682c9384efbbf58ff69a5afa006, title = "ERP measures of math anxiety: how math anxiety affects working memory and mental calculation tasks?", abstract = "There have been several attempts to account for the impact of Mathematical Anxiety (MA) on brain activity with variable results. The present study examines the effects of MA on ERP amplitude during performance of simple arithmetic calculations and working memory tasks. Data were obtained from 32 university students as they solved four types of arithmetic problems (one- and two-digit addition and multiplication) and a working memory task comprised of three levels of difficulty (1, 2, and 3-back task). Compared to the Low-MA group, High-MA individuals demonstrated reduced ERP amplitude at frontocentral (between 180-320 ms) and centroparietal locations (between 380-420 ms). These effects were independent of task difficulty/complexity, individual performance, and general state/trait anxiety levels. Results support the hypothesis that higher levels of self-reported MA are associated with lower cortical activation during the early stages of the processing of numeric stimuli in the context of cognitive tasks.", keywords = "EEG, ERPs, Mathematical anxiety, Mathematical cognition, Mental calculations, Working memory", author = "Klados, {Manousos A.} and Panagiotis Simos and Sifis Micheloyannis and Daniel Margulies and Bamidis, {Panagiotis D.}", note = "{\textcopyright} 2015 Klados, Simos, Micheloyannis, Margulies and Bamidis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.", year = "2015", month = oct, day = "26", doi = "10.3389/fnbeh.2015.00282", language = "English", volume = "9", journal = "Frontiers in Behavioral Neuroscience", issn = "1662-5153", publisher = "Frontiers Media S.A.", } . Frontiers in Behavioral Neuroscience.

Klados, M.A., Simos, P., Micheloyannis, S., Margulies, D., Bamidis, P.D.(2015). ERP measures of math anxiety: How math anxiety affects working memory and mental calculation tasks? . Frontiers in Behavioral Neuroscience. 9. (OCTOBER).

Manousos Klados, P. D. Bamidis, A. B. Vivas, C. Styliadis, C. Frantzidis, W. Schlee, A. Siountas, S. G. Papageorgiou(2014). A review of physical and cognitive interventions in aging . Neuroscience and Biobehavioral Reviews. 44. p. 206--220. Elsevier

Frantzidis, C.A., Vivas, A.B., Tsolaki, A., Klados, M.A., Tsolaki, M., Bamidis, P.D.(2014). Functional disorganization of small-world brain networks in mild Alzheimer's disease and amnestic Mild cognitive impairment: An EEG study using Relative Wavelet Entropy (RWE) . Frontiers in Aging Neuroscience. 6. (AUG).

Manousos Klados, Christos A. Frantzidis, Ana B. Vivas, Anthoula Tsolaki, Magda Tsolaki, Panagiotis D. Bamidis(2014). Functional disorganization of small-world brain networks in mild Alzheimer's disease and amnestic Mild cognitive impairment . Frontiers in Aging Neuroscience. 6. Frontiers Media S.A.

Bamidis, P.D., Vivas, A.B., Styliadis, C., Frantzidis, C., Klados, M., Schlee, W., Siountas, A., Papageorgiou, S.G.(2014). A review of physical and cognitive interventions in aging . Neuroscience and Biobehavioral Reviews. 44. p. 206-220.

Klados, M.A., Kanatsouli, K., Antoniou, I., Babiloni, F., Tsirka, V., Bamidis, P.D., Micheloyannis, S.(2013). A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks . PloS one. 8. (8).

Manousos Klados, Kassia Kanatsouli, Ioannis Antoniou, Fabio Babiloni, Vassiliki Tsirka, Panagiotis D. Bamidis, Sifis Micheloyannis(2013). A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks . PLoS ONE. 8. (8). Public Library of Science

Alcohol Affects the Brain's Resting-State Network in Social Drinkers @article{56fbc23febd34172962cdbee35d05de5, title = "Alcohol Affects the Brain's Resting-State Network in Social Drinkers", abstract = "Acute alcohol intake is known to enhance inhibition through facilitation of GABAA receptors, which are present in 40% of the synapses all over the brain. Evidence suggests that enhanced GABAergic transmission leads to increased large-scale brain connectivity. Our hypothesis is that acute alcohol intake would increase the functional connectivity of the human brain resting-state network (RSN). To test our hypothesis, electroencephalographic (EEG) measurements were recorded from healthy social drinkers at rest, during eyes-open and eyes-closed sessions, after administering to them an alcoholic beverage or placebo respectively. Salivary alcohol and cortisol served to measure the inebriation and stress levels. By calculating Magnitude Square Coherence (MSC) on standardized Low Resolution Electromagnetic Tomography (sLORETA) solutions, we formed cortical networks over several frequency bands, which were then analyzed in the context of functional connectivity and graph theory. MSC was increased (p<0.05, corrected with False Discovery Rate, FDR corrected) in alpha, beta (eyes-open) and theta bands (eyes-closed) following acute alcohol intake. Graph parameters were accordingly altered in these bands quantifying the effect of alcohol on the structure of brain networks; global efficiency and density were higher and path length was lower during alcohol (vs. placebo, p<0.05). Salivary alcohol concentration was positively correlated with the density of the network in beta band. The degree of specific nodes was elevated following alcohol (vs. placebo). Our findings support the hypothesis that short-term inebriation considerably increases large-scale connectivity in the RSN. The increased baseline functional connectivity can -at least partially- be attributed to the alcohol-induced disruption of the delicate balance between inhibitory and excitatory neurotransmission in favor of inhibitory influences. Thus, it is suggested that short-term inebriation is associated, as expected, to increased GABA transmission and functional connectivity, while long-term alcohol consumption may be linked to exactly the opposite effect.", author = "Chrysa Lithari and Klados, {Manousos A.} and Costas Pappas and Maria Albani and Dorothea Kapoukranidou and Leda Kovatsi and Bamidis, {Panagiotis D.} and Papadelis, {Christos L.}", note = "{\textcopyright} 2012 Lithari et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ", year = "2012", month = oct, day = "31", doi = "10.1371/journal.pone.0048641", language = "English", volume = "7", journal = "PLoS ONE", issn = "1932-6203", publisher = "Public Library of Science", number = "10", } . PLoS ONE.

How does the metric choice affect brain functional connectivity networks? @article{e05ec196d48b4c559c2a6c44380d7546, title = "How does the metric choice affect brain functional connectivity networks?", abstract = "Brain functional connectivity has gained increasing interest over the last few years. The application of Graph Theory on functional connectivity networks (FCNs) has shed light into different topics related to physiology as well as pathology. To this end, different connectivity metrics may be used; however, some concerns are often raised related with inconsistency of results and their associated neurophysiological interpretations depending on the metric used. This paper examines how the use of different connectivity metrics affects the small-world-ness of the FCNs and eventually the neuroscientific evidences and their interpretation; to achieve this, electroencephalography (EEG) data recorded from healthy subjects during an emotional paradigm are utilized. Participants passively viewed emotional stimuli from the international affective picture system (IAPS), categorized in four groups ranging in pleasure (valence) and arousal. Four different pair-wise metrics were used to estimate the connectivity between each pair of EEG channels: the magnitude square coherence (MSC), cross-correlation (CCOR), normalized mutual information (NMI) and normalized joint entropy (NJE). The small-world-ness is found to be varying among the connectivity metrics, while it was also affected by the choice of the threshold level. The use of different connectivity metrics affected the significance of the neurophysiological results. However, the results from different metrics were to the same direction: pleasant images exhibited shorter characteristic path length than unpleasant ones, while high arousing images were related to lower local efficiency as compared to the low arousing ones. Our findings suggest that the choice of different metrics modulates the small-world-ness of the FCNs as well as the neurophysiological results and should be taken into account when studying brain functional connectivity using graph theory.", keywords = "Connectivity metrics, EEG, Emotions, Functional connectivity networks, Graph theory", author = "C. Lithari and Klados, {M. A.} and C. Papadelis and C. Pappas and M. Albani and Bamidis, {P. D.}", year = "2012", month = may, doi = "10.1016/j.bspc.2011.05.004", language = "English", volume = "7", pages = "228--236", journal = "Biomedical Signal Processing and Control", issn = "1746-8094", publisher = "Elsevier", number = "3", } . Biomedical Signal Processing and Control.

Athanasiou, A., Lithari, C., Kalogianni, K., Klados, M.A., Bamidis, P.D.(2012). Source detection and functional connectivity of the sensorimotor cortex during actual and imaginary limb movement: A preliminary study on the implementation of econnectome in motor imagery protocols . Advances in Human-Computer Interaction. 2012.

Manousos Klados, Alkinoos Athanasiou, Chrysa Lithari, Konstantina Kalogianni, Panagiotis D. Bamidis(2012). Source Detection and Functional Connectivity of the Sensorimotor Cortex during Actual and Imaginary Limb Movement . Advances in Human-Computer Interaction. 2012. p. 1--10. Hindawi Limited

Lithari, C., Klados, M.A., Papadelis, C., Pappas, C., Albani, M., Bamidis, P.D.(2012). How does the metric choice affect brain functional connectivity networks? . Biomedical Signal Processing and Control. 7. (3). p. 228-236.

Lithari, C., Klados, M.A., Pappas, C., Albani, M., Kapoukranidou, D., Kovatsi, L., Bamidis, P.D., Papadelis, C.L.(2012). Alcohol Affects the Brain's Resting-State Network in Social Drinkers . PLoS ONE. 7. (10).

REG-ICA @article{0f57625101514eda80af11795e12c71d, title = "REG-ICA: A hybrid methodology combining Blind Source Separation and regression techniques for the rejection of ocular artifacts", abstract = "There are so far two main methodological approaches for rejecting ocular artifacts from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals: regression- and Blind Source Separation (BSS)-based techniques, both having merits, as well as, some serious limitations. In this piece of work, a hybrid methodology that combines the main advantages of these two methods is proposed. We hypothesize that the artifactual independent components (ICs) extracted by a BSS method include more ocular and less cerebral activity than the contaminated EEG signals. We thus propose to apply a regression algorithm to the ICs rather than directly to the recorded signals. The analysis was carried out with synthetic mixtures of real EEG and electroocculographic (EOG) recordings. A BSS method, the extended INFOMAX version of Independent Component Analysis (ICA), was initially used to decompose the artificially contaminated EEG signals into spatiotemporal ICs. Then, a regression scheme, based on a stable version of the Recursive Least Squares algorithm, sRLS, was applied to the artifactual components in order to remove only the ocular artifacts, maintaining the underlying neural signals intact. The processed ICs were then projected back, reconstructing the artifact-free EEG signals. The performance of the proposed technique was compared with two automatic techniques; a regression technique based on Least Mean Square (LMS) algorithm and a BSS-based artifact rejection technique called wavelet-ICA (W-ICA) on the artificially contaminated data. For comparison, two metrics were used to assess the different methods' performance: the first quantified how successful each technique was in removing the ocular artifacts from the EEG recordings, and the second one quantified how much each technique distorted the ongoing brain activity in both time and frequency domains. Confirming our main hypothesis, results have shown that the artifactual ICs contained more ocular and less cerebral activity (p < 0.04) (artifact to signal ratio (ASR) = 1.83 ± 3.65) in contrast to the contaminated electrode signals (ASR = 0.69 ± 3.40). Our results reveal that the proposed methodology, namely REG-ICA, removes the ocular artifacts more successfully than W-ICA (p < 0.01) or LMS (p < 0.01). It also distorts less the brain activity in the time domain when compared to W-ICA and LMS. In the frequency domain, it distorts the brain activity less than the W-ICA in all frequency bands, and less than the LMS for the delta, beta, and gamma bands. Our results suggest that the proposed methodology is evidently an attractive alternative to other already proposed artifact rejection methodologies.", keywords = "Adaptive filter, EEG, EOG, EOG artifact rejection, ICA, REG-ICA, Regression analysis", author = "Klados, {Manousos A.} and Christos Papadelis and Christoph Braun and Bamidis, {Panagiotis D.}", year = "2011", month = jul, doi = "10.1016/j.bspc.2011.02.001", language = "English", volume = "6", pages = "291--300", journal = "Biomedical Signal Processing and Control", issn = "1746-8094", publisher = "Elsevier", number = "3", } . Biomedical Signal Processing and Control.

Klados, M.A., Papadelis, C., Braun, C., Bamidis, P.D.(2011). REG-ICA: A hybrid methodology combining Blind Source Separation and regression techniques for the rejection of ocular artifacts . Biomedical Signal Processing and Control. 6. (3). p. 291-300.

Does arousal and valence affect ERPs and brain connectivity? A study during an emotional paradigm @article{f4e80a556f204ab7b458ba12c98138d4, title = "Does arousal and valence affect ERPs and brain connectivity? A study during an emotional paradigm", author = "C. Lithari and C. Frantzidis and M.a. Klados and P.d. Bamidis", year = "2010", month = sep, day = "1", doi = "10.1016/j.ijpsycho.2010.06.101", language = "English", volume = "77", pages = "266--266", journal = "International Journal of Psychophysiology", issn = "0167-8760", publisher = "Elsevier", number = "3", } . International Journal of Psychophysiology.

On the Classification of Emotional Biosignals Evoked While Viewing Affective Pictures: An Integrated Data-Mining-Based Approach for Healthcare Applications @article{8927698139414fb6b33c0031e1d0aa81, title = "On the Classification of Emotional Biosignals Evoked While Viewing Affective Pictures: An Integrated Data-Mining-Based Approach for Healthcare Applications", author = "C.a. Frantzidis and C. Bratsas and M.a. Klados and E. Konstantinidis and C.d. Lithari and A.b. Vivas and C.l. Papadelis and E. Kaldoudi and C. Pappas and P.d. Bamidis", year = "2010", month = mar, day = "1", doi = "10.1109/TITB.2009.2038481", language = "English", volume = "14", pages = "309--318", journal = "IEEE Transactions on Information Technology in Biomedicine", issn = "1089-7771", publisher = "IEEE", number = "2", } . IEEE Transactions on Information Technology in Biomedicine.

Are females more responsive to emotional stimuli? A neurophysiological study across arousal and valence dimensions @article{f910c2f8c85a47d39c87582f2f938892, title = "Are females more responsive to emotional stimuli? A neurophysiological study across arousal and valence dimensions", abstract = "Men and women seem to process emotions and react to them differently. Yet, few neurophysiological studies have systematically investigated gender differences in emotional processing. Here, we studied gender differences using Event Related Potentials (ERPs) and Skin Conductance Responses (SCR) recorded from participants who passively viewed emotional pictures selected from the International Affective Picture System (IAPS). The arousal and valence dimension of the stimuli were manipulated orthogonally. The peak amplitude and peak latency of ERP components and SCR were analyzed separately, and the scalp topographies of significant ERP differences were documented. Females responded with enhanced negative components (N100 and N200), in comparison to males, especially to the unpleasant visual stimuli, whereas both genders responded faster to high arousing or unpleasant stimuli. Scalp topographies revealed more pronounced gender differences on central and left hemisphere areas. Our results suggest a difference in the way emotional stimuli are processed by genders: unpleasant and high arousing stimuli evoke greater ERP amplitudes in women relatively to men. It also seems that unpleasant or high arousing stimuli are temporally prioritized during visual processing by both genders.", keywords = "Emotions, Event related potentials, Gender differences, Skin conductance", author = "C. Lithari and Frantzidis, {C. A.} and C. Papadelis and Vivas, {Ana B.} and Klados, {M. A.} and C. Kourtidou-Papadeli and C. Pappas and Ioannides, {A. A.} and Bamidis, {P. D.}", note = "{\textcopyright}The Author(s) 2009. This article is published with open access at Springerlink.com", year = "2010", month = mar, doi = "10.1007/s10548-009-0130-5", language = "English", volume = "23", pages = "27--40", journal = "Brain Topography", issn = "0896-0267", publisher = "Kluwer", number = "1", } . Brain Topography.

Lithari, C., Frantzidis, C.A., Papadelis, C., Vivas, A.B., Klados, M.A., Kourtidou-Papadeli, C., Pappas, C., Ioannides, A.A., Bamidis, P.D.(2010). Are females more responsive to emotional stimuli? A neurophysiological study across arousal and valence dimensions . Brain Topography. 23. (1). p. 27-40.

Frantzidis, C.A., Bratsas, C., Klados, M.A., Konstantinidis, E., Lithari, C.D., Vivas, A.B., Papadelis, C.L., Kaldoudi, E., Pappas, C., Bamidis, P.D.(2010). On the classification of emotional biosignals evoked while viewing affective pictures: An integrated data-mining-based approach for healthcare applications . IEEE Transactions on Information Technology in Biomedicine. 14. (2). p. 309-318.

A Framework Combining Delta Event-Related Oscillations (EROs) and Synchronisation Effects (ERD/ERS) to Study Emotional Processing @article{0f1c707d316c4ed1a501e16531f95263, title = "A Framework Combining Delta Event-Related Oscillations (EROs) and Synchronisation Effects (ERD/ERS) to Study Emotional Processing", abstract = "Event-Related Potentials (ERPs) or Event-Related Oscillations (EROs) have been widely used to study emotional processing, mainly on the theta and gamma frequency bands. However, the role of the slow (delta) waves has been largely ignored. The aim of this study is to provide a framework that combines EROs with Event-Related Desynchronization (ERD)/Event-Related Synchronization (ERS), and peak amplitude analysis of delta activity, evoked by the passive viewing of emotionally evocative pictures. Results showed that this kind of approach is sensitive to the effects of gender, valence, and arousal, as well as, the study of interhemispherical disparity, as the two-brain hemispheres interplay roles in the detailed discrimination of gender. Valence effects are recovered in both the central electrodes as well as in the hemisphere interactions. These findings suggest that the temporal patterns of delta activity and the alterations of delta energy may contribute to the study of emotional processing. Finally the results depict the improved sensitivity of the proposed framework in comparison to the traditional ERP techniques, thereby delineating the need for further development of new methodologies to study slow brain frequencies.", author = "Klados, {Manousos A.} and Christos Frantzidis and Vivas, {Ana B.} and Christos Papadelis and Chrysa Lithari and Costas Pappas and Bamidis, {Panagiotis D.}", note = "{\textcopyright} 2009 Manousos A. Klados et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.", year = "2009", month = jan, day = "1", doi = "10.1155/2009/549419", language = "English", volume = "2009", journal = "Computational Intelligence and Neuroscience", issn = "1687-5265", publisher = "Hindawi Limited", } . Computational Intelligence and Neuroscience.

Bamidis, P.D., Klados, M.A., Frantzidis, C., Vivas, A.B., Papadelis, C., Lithari, C., Pappas, C.(2009). A framework combining delta event-related oscillations (EROs) and synchronisation effects (ERD/ERS) to study emotional processing . Computational Intelligence and Neuroscience. 2009.

Manousos Klados, Charalampos Bratsas, Christos A Frantzidis, Christos Papadelis, Costas Pappas, Panagiotis D Bamidis(2009). Towards a semantic framework for an integrative description of neuroscience patterns and studies: a case for emotion-related data . Studies in health technology and informatics. 150. p. 322--326. IOS

Manousos A. Klados, Christos Frantzidis, Ana B. Vivas, Christos Papadelis, Chrysa Lithari, Costas Pappas, Panagiotis D. Bamidis, 2009, 'A Framework Combining Delta Event-Related Oscillations (EROs) and Synchronisation Effects (ERD/ERS) to Study Emotional Processing', <i>Computational Intelligence and Neuroscience</i>, vol. 2009, no. 3, pp. 1-16 .

Chrysa Lithari, Manousos A. Klados, Costas Pappas, Maria Albani, Dorothea Kapoukranidou, Leda Kovatsi, Panagiotis D. Bamidis, Christos L. Papadelis, Gareth Robert Barnes, 2012, 'Alcohol Affects the Brain's Resting-State Network in Social Drinkers', <i>PLoS ONE</i>, vol. 7, no. 10 .

Christos Frantzidis, Anastasia Semertzidou, Aristea Ladas, Maria Karagianni, Chrysa Lithari, Athina Kyrillidou, Eirini Grigoriadou, Manousos A. Klados, Ana Vivas, Fotini Kounti, Magda Tsolaki, Costas Pappas, Panagiotis Bamidis, 2011, 'Detecting neurophysiological alterations during Mild Cognitive Impairment and Dementia using wavelet-based energy computation and a Mahalanobis Distance classifier', <i>Neuroscience Letters</i>, vol. 500, p. e53 .

Christos Moridis, Manousos A. Klados, Vasileios Terzis, Anastasios Economides, Anna Karlovasitou, Panagiotis Bamidis, Vasileios Karabatakis, 2011, 'Audiovisual stimulation to influence alpha brain oscillations: An EEG study of gender differences', <i>Neuroscience Letters</i>, vol. 500, p. e51 .

C. Lithari, C. Frantzidis, M.A. Klados, P.D. Bamidis, 2010, 'Does arousal and valence affect ERPs and brain connectivity? A study during an emotional paradigm', <i>International Journal of Psychophysiology</i>, vol. 77, no. 3, pp. 266-266 .

C. Lithari, M.A. Klados, C. Papadelis, C. Pappas, M. Albani, P.D. Bamidis, 2012, 'How does the metric choice affect brain functional connectivity networks?', <i>Biomedical Signal Processing and Control</i>, vol. 7, no. 3 .

Manousos A. Klados, Christos Papadelis, Christos Frantzidis, Panagiotis Bamidis, 2011, 'Is the Artifact Rejection enhanced if the EOG signals are included in the ICA decomposition?', <i>Neuroscience Letters</i>, vol. 500, pp. e50-e51 .

Manousos A. Klados, Christos Papadelis, Christoph Braun, Panagiotis D. Bamidis, 2011, 'REG-ICA: A hybrid methodology combining Blind Source Separation and regression techniques for the rejection of ocular artifacts', <i>Biomedical Signal Processing and Control</i>, vol. 6, no. 3 .

Alkinoos Athanasiou, Chrysa Lithari, Konstantina Kalogianni, Manousos A. Klados, Panagiotis D. Bamidis, 2012, 'Source Detection and Functional Connectivity of the Sensorimotor Cortex during Actual and Imaginary Limb Movement: A Preliminary Study on the Implementation of eConnectome in Motor Imagery Protocols', <i>Advances in Human-Computer Interaction</i>, vol. 2012, no. 3 .

PREPRINT

Sepehr Mortaheb, Laurens Van Calster, Federico Raimondo, Manousos A. Klados, Paradeisios Alexandros Boulakis, Kleio Georgoula, Steve Majerus, Dimitri Van De Ville, Athena Demertzi(2021). Mind blanking is a distinct mental state linked to a recurrent brain profile of globally positive connectivity during ongoing mentation . Cold Spring Harbor Laboratory

OTHER

Sepehr Mortaheb, Manousos A. Klados, Laurens Van Calster, Paradeisios Alexandros Boulakis, Kleio Georgoula, Steve Majerus, Athena Demertzi(2021). Mind blanking is associated with a rigid spatio-temporal profile in typical wakefulness . Cold Spring Harbor Laboratory

Manousos Klados, Vasileios Rafail Xefteris, Charis Styliadis, Alexandra Anagnostopoulou, Panagiotis Kartsidis, Evangelos Paraskevopoulos, Vasiliki Zilidou, Maria Karagianni, Panagiotis D. Bamidis(2020). Computerized physical exercise improves the functional architecture of the brain in patients with Parkinson’s Disease: a network science resting-state EEG study . Cold Spring Harbor Laboratory

Manousos Klados, Alexandra Anagnostopoulou, Charis Styliadis, Panagiotis Kartsidis, Evangelia Romanopoulou, Vasiliki Zilidou, Chrysi Karali, Maria Karagianni, Evangelos Paraskevopoulos, Panagiotis D. Bamidis(2020). Computerized physical and cognitive training improves the functional architecture of the brain in adults with Down Syndrome: a network science EEG study . Cold Spring Harbor Laboratory

Manousos Klados, Christiane M. Nday, Christina E. Plomariti, Vasilis D. Nigdelis, Giorgos Ntakakis, Panagiotis D. Bamidis(2020). Current trends of biomedical signal processing in neuroscience . Neurotechnology: Methods, advances and applications. p. 7--36. Institution of Engineering and Technology

Georgios A. Klados, Michalis Zervakis, Rosalia Dacosta-Aguayo, Antonio Fratini, Manousos A. Klados(2019). Towards a Novel Way to Predict Deficits After a Brain Lesion: A Stroke Example . 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). {IEEE}

Manousos Klados, Matthew Fenech, Stefano Seri(2019). High-Frequency Oscillations in Epilepsy: A Short Review . 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). {IEEE}

Manousos Klados, Katerina Giannakaki, Giorgos Giannakakis, Pelagia Vorgia, Michalis Zervakis(2019). Automatic Absence Seizure Detection Evaluating Matching Pursuit Features of EEG Signals . 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). {IEEE}

CONFERENCE PAPER

Automatic Absence Seizure Detection Evaluating Matching Pursuit Features of EEG Signals @inproceedings{67f11fd1856c4150b64b48fb381943c2, title = "Automatic Absence Seizure Detection Evaluating Matching Pursuit Features of EEG Signals", abstract = "This paper evaluates the usage of matching pursuit (MP) features of electroencephalographic (EEG) signals and classification techniques on automatic absence seizure detection. Absence epileptic seizures are neurological disorders which are manifested as abnormal EEG patterns. Matching pursuit algorithm is able to decompose a signal into components with specific time-frequency characteristics. It is a robust technique especially when there is complex, multicomponent signal. In the present study, a clinical dataset containing 40 annotated absence seizures in long-term EEG recordings from pediatric epileptic patients (with age 6.0±2.9 years) was analyzed. The extracted MP features fed an automatic classification schema which achieved a time window based discrimination accuracy of 98.5%. As indicated by the study's results, the proposed features and analysis methods can be a promising addition to the area of automatic absence seizures detection.", author = "K. Giannakaki and G. Giannakakis and P. Vorgia and Manousos Klados and Zervakis, {Michalis E.}", year = "2019", month = dec, day = "26", doi = "10.1109/BIBE.2019.00165", language = "English", isbn = "978-1-7281-4618-8", series = "2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)", publisher = "IEEE", booktitle = "2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)", address = "United States", note = "2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). ; Conference date: 28-10-2019 Through 30-10-2019", } . 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE).

High-Frequency Oscillations in Epilepsy: A Short Review @inproceedings{18a7e59ceec845108e0df94abf75199d, title = "High-Frequency Oscillations in Epilepsy: A Short Review", abstract = "High-Frequency Oscillations (HFOs) are current strong candidates to serve as biomarkers for the seizure-onset zone (SOZ) in epilepsy. The emergence of new technology and digital methods benefits epilepsy research with the identification and characterizing the SOZ by using HFOs. Invasive recordings, together with automatic detection methods, are at the forefront of epilepsy research, especially when surgery is inevitable for seizure-freedom. However, recent non-invasive HFOs recordings quickly gathered attention for validation to be implemented in future clinical research and practice. This short review aims to briefly provide the research findings regarding HFOs and their significance as biomarkers of epileptogenicity.", author = "Matthew Fenech and Stefano Seri and Manousos Klados", note = "Funding: European Union{\textquoteright}s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 823958.; 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). ; Conference date: 28-10-2019 Through 30-10-2019", year = "2019", month = dec, day = "26", doi = "10.1109/BIBE.2019.00164", language = "English", isbn = "978-1-7281-4618-8", series = "2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)", publisher = "IEEE", booktitle = "2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)", address = "United States", } . 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE).

Towards a Novel Way to Predict Deficits After a Brain Lesion: A Stroke Example @inproceedings{ecf6522ebb48486d8bacc4121e8232fa, title = "Towards a Novel Way to Predict Deficits After a Brain Lesion: A Stroke Example", abstract = "Many studies have addressed the relations between different human brain regions and their role in cognitive, motor and sensory functions in patients that have suffered a brain lesion (stroke, traumatic brain injury, tissue removal). Nowadays, it is well established that the brain works as a network and the symptoms in a person are a combination of the direct impact of the lesion in a single region and its connectivity with other healthy brain regions. The aim of the present study is the development of a user-friendly desktop application to predict the induced cognitive deficits in patients who have suffered a brain lesion. The herein presented application is based on Neurosynth platform, and takes as an input a MRI mask that describes a lesion. Then our software exploits the knowledge that already exists in Neurosynth platform, so as to predict the potential deficits by grouping the Neurosynth's terms that have increased Z scores with our mask. In addition, we have embedded two types of visualization methods: One to present the slices of the brain mask and another to show the 3D volume of the mask into 3D semitransparent human brain. The added value of the presented application is that it may give us a clue about which mechanisms are probably affected by a lesion in a specific region, while in the future it could provide neurosurgeons with insightful knowledge helping them in the plannification of a forthcoming surgical procedure. The proposed software was tested on 7 stroke patients, predicting accurately the 91% of the measured deficits found during a neuropsychological assessment.", author = "Georgios Klados and Zervakis, {Michalis E.} and Rosalia Dacosta-Aguayo and Antonio Fratini and Manousos Klados", note = "{\textcopyright} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). ; Conference date: 28-10-2019 Through 30-10-2019", year = "2019", month = dec, day = "26", doi = "10.1109/BIBE.2019.00138", language = "English", isbn = "978-1-7281-4618-8", series = "2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)", publisher = "IEEE", booktitle = "2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)", address = "United States", } . 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE).

FCLAB @inproceedings{5688646128f94130851978d05f50ab99, title = "FCLAB: An EEGLAB module for performing functional connectivity analysis on single-subject EEG data", abstract = "Functional connectivity (FC) analysis constitutes a fundamental neuroscientific approach that has been extensively used for the investigation of brain's connectivity and activation patterns. To that end, several software tools have been developed. This paper presents FCLAB, the only EEGLAB-based plugin, which is able to work with EEG signals in order to estimate and visualize brain functional connectivity networks based on a variety of similarity measures as well as run a complete graph analysis procedure followed by a detailed visualization of the ensuing local and global measures distribution. FCLAB entails optimization procedures for the implementation of the connectivity structures and is the result of long-term research in EEG functional connectivity. The computed functional connectivity measures have been carefully selected to reflect the state-of-art in the field. Future work focuses on extending the platform for multi-subject analysis in order to enable the implementation of statistical analysis tools.", keywords = "EEG, functional connectivity analysis, graph analysis", author = "Pezoulas, {Vasileios C} and Alkinoos Athanasiou and Guido Nolte and Zervakis, {Michalis E.} and Antonio Fratini and Dimitrios Fotiandis and Manousos Klados", note = "{\textcopyright} 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ", year = "2018", month = apr, day = "9", doi = "10.1109/BHI.2018.8333378", language = "English", pages = "96--99", booktitle = "2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)", publisher = "IEEE", address = "United States", } . 2018 IEEE EMBS International Conference on Biomedical &amp; Health Informatics (BHI).

An Automatic EEG Based System for the Recognition of Math Anxiety @inproceedings{6591b7c746e54beba18a8edf6fbe1e93, title = "An Automatic EEG Based System for the Recognition of Math Anxiety", abstract = "Mathematical Anxiety is the feeling of fear or dislike when dealing with mathematical rich situations. Although math anxiety seems to be innocent it can seriously affect so the learning procedure, as the future carrier directions. The accurate recognition of math anxiety is very important so for diagnostic purposes as for e-learning systems. This work comes to present an automatic system for the detection of math anxiety based on electroencephalographic (EEG) signals, that are supposed to be more subjective, compared to self-report and psychometric questionnaires, since they cannot be intentionally modulated. For this reason we have gathered multichannel EEG recordings from two groups with different levels of math anxiety (Low and High). From these EEG signals we have extracted 466 features and then using a feature selection algorithm we ended to only one feature that was able to recognize math anxiety with 93.75% accuracy using a Naive Bayesian Tree with 10-fold cross validation.", keywords = "Automatic Recognition of Anxiety, EEG, Math Anxiety, Mathematical Cognition", author = "Klados, {Manousos A.} and Niki Pandria and Alkinoos Athanasiou and Bamidis, {Panagiotis D.}", year = "2017", month = nov, day = "13", doi = "10.1109/CBMS.2017.107", language = "English", volume = "2017-June", series = "Proceedings IEEE International Symposium on Computer-Based Medical Systems", publisher = "IEEE", pages = "409--412", booktitle = "Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017", address = "United States", note = "30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017 ; Conference date: 22-06-2017 Through 24-06-2017", } . Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017.

Predicting Cognitive Recovery of Stroke Patients from the Structural MRI Connectome Using a Naïve Bayesian Tree Classifier @inproceedings{efc31a58559044d59a459b9b036211db, title = "Predicting Cognitive Recovery of Stroke Patients from the Structural MRI Connectome Using a Na{\"i}ve Bayesian Tree Classifier", abstract = "Successful post-stroke prognosis and recovery strategies are heavily dependent on our understanding about how the damage to one specific region may impact to other remote regions, as well as the various functional networks involved in efficient cognitive function. In this study, 27 consecutive ischemic stroke patients were recruited. Stroke patients underwent two complete neuropsychological assessments between the first 72 hours after stroke arrival and three months later. They were further evaluated with a MRI protocol at 3 months. Patients were splitted into two groups according to their level of cognitive recovery. A data mining technique was then applied to the probabilistic tractography data in order to determine whether the structural connectivity features can efficiently classify good from poor recovery. We found that the connectivity probability between the left Superior Parietal Gyrus and the left Angular Gyrus can describe the cognitive classification (good versus poor recovery) after stroke. Both regions are involved in higher cognitive functioning and their dysfunction has been related to mild cognitive impairment and dementia. Our findings suggest that cognitive prognosis, in stroke patients, mainly depends on the connection of these two regions. An accurate model for the early prediction of stroke recovery as the one presented herein is fundamental to develop early personalized rehabilitation strategies.", keywords = "cognitive recovery, Na{\"i}ve Bayesian Tree, Stroke, structural connectome", author = "Rosalia Dacosta-Aguayo and Christian Stephan-Otto and Tibor Auer and Ic Clemente and Antoni Davalos and Nuria Bargallo and Maria Mataro and Klados, {Manousos A.}", year = "2017", month = nov, day = "10", doi = "10.1109/CBMS.2017.106", language = "English", volume = "2017-June", series = "Proceedings IEEE International Symposium on Computer-Based Medical Systems", publisher = "IEEE", pages = "413--418", booktitle = "Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017", address = "United States", note = "30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017 ; Conference date: 22-06-2017 Through 24-06-2017", } . Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017.

State of the art and future prospects of nanotechnologies in the field of brain-computer interfaces @inproceedings{03ecdcb0d2524dba8f9b43f1d4dcbd53, title = "State of the art and future prospects of nanotechnologies in the field of brain-computer interfaces", abstract = "Neuroprosthetic control by individuals suffering from tetraplegia has already been demonstrated using implanted microelectrode arrays over the patients{\textquoteright} motor cortex. Based on the state of the art of such micro & nano-scale technologies, we review current trends and future prospects for the implementation of nanotechnologies in the field of Brain- Computer Interfaces (BCIs), with brief mention of current clinical applications. Micro- and Nano-Electromechanical Systems (MEMS, NEMS) and micro-Electrocorticography now belong to the mainstay of neurophysiology, producing promising results in BCI applications, neurophysiological recordings and research. The miniaturization of recording and stimulation systems and the improvement of reliability and durability, decrease of neural tissue reactivity to implants, as well as increased fidelity of said systems are the current foci of this technology. Novel concepts have also begun to emerge such as nanoscale integrated circuits that communicate with the macroscopic environment, neuronal pattern nano-promotion, multiple biosensors that have been “wired” with piezoelectric nanomechanical resonators, or even “neural dust” consisting of 10-100μm scale independent floating low-powered sensors. Problems that such technologies have to bypass include a minimum size threshold and the increase in power to maintain a high signal-to-noiseratio. Physiological matters such as immunological reactions, neurogloia or neuronal population loss should also be taken into consideration. Progress in scaling down of injectable interfaces to the muscles and peripheral nerves is expected to result in less invasive BCI-controlled actuators (neuroprosthetics in the micro and nano scale). The state-of-the-art of current microtechnologies demonstrate a maturing level of clinical relevance and promising results in terms of neural recording and stimulation. New MEMS and NEMS fabrication techniques and novel design and application concepts hold promise to address current problems with these technologies and lead to less invasive, longer lasting and more reliable BCI systems in the near future.", keywords = "Brain computer interface, Microelectrode, Nanoscale, Nanotechnology, Neuroprosthetics", author = "Alkinoos Athanasiou and Klados, {M. A.} and A. Astaras and N. Foroglou and I. Magras and Bamidis, {P. D.}", year = "2016", month = sep, day = "17", doi = "10.1007/978-3-319-32703-7_89", language = "English", isbn = "9783319327013", volume = "57", series = "IFMBE Proceedings", publisher = "Springer", pages = "456--460", booktitle = "XIV Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016", address = "Germany", note = "14th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016 ; Conference date: 31-03-2016 Through 02-04-2016", } . XIV Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016.

Characterizing the Primary Spectrum of Personality and Brain Connectivity @conference{3f63d3b978f74677a073e1573344bc58, title = "Characterizing the Primary Spectrum of Personality and Brain Connectivity", author = "Manousos Klados and Mark Lauckner and Estrid Jackobsen and Margulies Daniel", year = "2016", month = jun, day = "30", language = "English", note = "22nd Annual Meeting of the Organization for Human Brain Mapping ; Conference date: 26-06-2016 Through 30-06-2016", url = "https://www.humanbrainmapping.org/i4a/pages/index.cfm?pageid=3701", } . 22nd Annual Meeting of the Organization for Human Brain Mapping, 26/06/16.

Athanasiou, A., Klados, M.A., Astaras, A., Foroglou, N., Magras, I., Bamidis, P.D.(2016). State of the art and future prospects of nanotechnologies in the field of brain-computer interfaces . IFMBE Proceedings. 57. p. 456-460.

Klados, M.A., Styliadis, C., Bamidis, P.D.(2014). A short review on emotional recognition based on biosignal pattern analysis . IFMBE Proceedings. 41. p. 787-790.

Manousos Klados, Charalampos Styliadis, Panagiotis D. Bamidis(2014). A short review on emotional recognition based on biosignal pattern analysis . 13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013 - MEDICON 2013. 41. p. 787--790. Springer

Bamparopoulos, G., Klados, M.A., Papathanasiou, N., Antoniou, I., Micheloyannis, S., Bamidis, P.D.(2014). Studying Functional Brain Networks to Understand Mathematical Thinking: A Graph-Theoretical Approach . IFMBE Proceedings. 41. p. 783-786.

Manousos Klados, Georgios Bamparopoulos, Nikolaos Papathanasiou, Ioannis Antoniou, Sifis Micheloyannis, PanagiotisD. D. Bamidis(2014). Studying Functional Brain Networks to Understand Mathematical Thinking: A Graph-Theoretical Approach . XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013. p. 783--786. Springer

Klados, M.A., Nikolaidou, M., Konstantinidis, E., Chifari, A., Bamidis, P.D.(2013). A short review of computerized monitoring systems for ADHD . Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems. p. 556-557.

Klados, M.A., Nikolaidou, M., Konstantinidis, E., Chifari, A., Bamidis, P.D.(2013). A short review of computerized monitoring systems for ADHD . Proceedings - IEEE Symposium on Computer-Based Medical Systems. p. 556-557.

Manousos Klados, Maria Nikolaidou, Evdokimos Konstantinidis, Antonella Chifari, Panagiotis D. Bamidis(2013). A short review of computerized monitoring systems for ADHD . Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems. p. 556--557. IEEE

Towards a graph theoretical approach to study gender lateralization effect in mathematical thinking @conference{2c63a8682110469caedc45d92c1f9e08, title = "Towards a graph theoretical approach to study gender lateralization effect in mathematical thinking", abstract = "Gender differences in mathematical thinking is a common concern of scientists from different research fields. Both parents and teachers report that males seem to perform better in complex mathematics compared to females. This study comes to shed light in the different organization of the underlying functional networks, in order to investigate the aforementioned observation, without supporting or rejecting this statement. In this sense, it is generally accepted that females use their both hemispheres to accomplish a certain task, while males use mostly the hemisphere which is properly suited. For the purposes of the current analysis, electroencephalographic recordings were collected from 11 males and 11 females, during a difficult mathematical task. Then a previously proposed model was used in order to pass from the sensor level to the cortical one, in order to examine the networks formed among the cortical dipoles. Mutual information was employed to form the graphs represeting the functional connectivity among the different dipoles, while the density, the global and the local efficiencies were further examined. The results suggest that females use their both hemisphere to solve the complex mathematical task while males use mostly their left hemisphere which is the responsible one for the mathematical thinking.", keywords = "Brain modeling, Educational institutions, Electroencephalography, Graph Theory, Inverse Problem, Mathematical Cognition, Mathematics, Mutual Information, Organizations, Scalp, biological techniques, complex mathematics, cortical dipoles, electroencephalographic recordings, functional connectivity, functional networks, gender lateralization effect, graph theoretical approach, mathematical task, mathematical thinking, neurophysiology", author = "Klados, {Manousos A.} and Chrysa Lithari and Ioannis Antoniou and Anastasia Semertzidou and Charalambos Bratsas and Sifis Micheloyannis and Bamidis, {Panagiotis D.}", year = "2012", month = nov, doi = "10.1109/BIBE.2012.6399746", language = "English", pages = "666--670", } .

Manousos Klados, Christos A. Frantzidis, Maria D. Diamantoudi, Eirini Grigoriadou, Anastasia Semertzidou, Antonis Billis, Evdokimos Konstantinidis, Ana B. Vivas, Charalampos Bratsas, Magda Tsolaki, et al.(2012). A Mahalanobis distance based approach towards the reliable detection of geriatric depression symptoms co-existing with cognitive decline . Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops. 382 AICT. (PART 2). p. 16--25.

Klados, M.A., Lithari, C., Antoniou, I., Semertzidou, A., Bratsas, C., Micheloyannis, S., Bamidis, P.D.(2012). Towards a graph theoretical approach to study gender lateralization effect in mathematical thinking . IEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012. p. 666-670.

The impact of audio-visual stimulation on alpha brain oscillations: An EEG study @inproceedings{dfa711dc3f0e4a5eade12d76b25acb5a, title = "The impact of audio-visual stimulation on alpha brain oscillations: An EEG study", abstract = "Many studies investigated the brain responses as a reaction in auditory or visual stimuli separately. However a few studies have been published so far investigating the interactions of the two aforementioned stimuli. The current study comes to examine the impact of the audio-visual stimulation with binaural beats and flickering light in four different colors on low and upper alpha oscillations. For this purpose electroencephalogram (EEG) was adopted and Event Related Desynchronization/Event Related Synchronization (ERD/ERS) has been used as an index in order to investigate the alpha brain responses. Statistically significant results suggest that the combination of audio-visual stimuli with binaural beats and flickering light color at 8 and 10 Hz respectively can evoke significant Following Frequency Response (FFR) of the low and upper alpha oscillations.", author = "Moridis, {Christos N.} and Klados, {Manousos A.} and Kokkinakis, {Ioannis A.} and Vasileios Terzis and Economides, {Anastasios A.} and Anna Karlovasitou and Bamidis, {Panagiotis D.} and Karabatakis, {Vasileios E.}", year = "2010", month = nov, doi = "10.1109/ITAB.2010.5687651", language = "English", pages = "1--4", booktitle = "2010 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB)", publisher = "IEEE", address = "United States", note = "2010 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB) ; Conference date: 03-11-2010 Through 05-11-2010", } . 2010 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB).

Small-world properties of brain Functional Connectivity Networks are affected by emotional stimuli @inproceedings{a022da0a87b0422dac25905db322027f, title = "Small-world properties of brain Functional Connectivity Networks are affected by emotional stimuli", abstract = "The aim of this study was to examine whether Functional Connectivity Networks (FCN) obtained during exposing the humans to emotional stimuli, vary according to the characteristics of the stimuli. Twenty-eight participants passively viewed emotional pictures from International Affective Picture System (IAPS) ranging across both arousal and pleasure dimensions. Electroencephalographic (EEG) signals were simultaneously recorded from 19 scalp sites. Relative Wavelet Entropy (RWE) was estimated for all EEG electrode pairs. Different RWE thresholds were set in order to form the corresponding FCNs. Graph theory metrics, such as Density, Cluster Coefficient and Global Efficiency were calculated for all FCNs. It was found that arousal significantly affected small-world properties of FCNs formed from 42 to 170 msec; low arousing pictures elicited FCN with higher Density, higher Cluster Coefficient and higher Global Efficiency as compared to high arousing ones.", author = "C. Lithari and Frantzidis, {C.A. A} and C. Papadelis and Klados, {M.A. A} and C. Pappas and Bamidis, {P.D. D}", year = "2010", month = nov, doi = "10.1109/ITAB.2010.5687815", language = "English", booktitle = "2010 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB)", publisher = "IEEE", address = "United States", note = "2010 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB) ; Conference date: 03-11-2010 Through 05-11-2010", } . 2010 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB).

Komnidis, A., Konstantinidis, E., Stylianou, I., Klados, M.A., Kalfas, A., Bamidis, P.D.(2010). A modular architecture of a computer-operated olfactometer for universal use . IFMBE Proceedings. 29. p. 280-283.

A Kurtosis-Based Automatic System Using Naïve Bayesian Classifier to Identify ICA Components Contaminated by EOG or ECG Artifacts @inproceedings{252e92736a594d55a03f68596ed746ab, title = "A Kurtosis-Based Automatic System Using Na{\"i}ve Bayesian Classifier to Identify ICA Components Contaminated by EOG or ECG Artifacts", abstract = "Electrical signals detected along the scalp by an Electroencephalogram (EEG), but that originate from non-cerebral origin are called artifacts. Especially when these artifacts are produced by the human body we talk about biological artifacts. The most common biological artifacts are the electrical signals produced by ocular and heart activity. EEG data is almost always contaminated by such artifacts. The last decade Independent Component Analysis (ICA) has a crucial role in neuroscience and it takes great attention for artifact rejection purposes. According to ICA{\textquoteright}s methodology, EEG signals are decomposed to statistical Independent Components (IC) and then an EEG specialist is called to recognize the artifactual ICs. Some of the major limitations of the current approach are that the aforementioned selection is subjective, it demands a high skill EEG operator, it is time consuming and it cannot be applied in online processing. Our study employs machine learning techniques in order to recognize the contaminated ICs with ocular or heart artifacts. More specific 19-channel EEG datasets from 86 normal subjects were decomposed using ICA (19×86=1634 ICs in total). Then three independent observers marked an IC as artifactual if it includes ocular or heart artifacts, otherwise it was marked as normal. Then kurtosis was computed in short segments with 1250 sample points fixed length without overlap for each IC. The mean kurtosis value was computed for each IC and the Na{\"i}ve Bayes Classifier (NBC) classifier was adopted in order to classify the ICs as artifactual or normal. The results suggest that the NBC has correctly classified 1611/1634 ICs (98.5924 %) so it can be suggested that kurtosis seems to be convenient for the classification of contaminated ICs by ocular or heart artifacts.", keywords = "ecg, eog, ica, na{\"i}ve bayes classifier", author = "Klados, {M. A.} and C. Bratsas and C. Frantzidis and Papadelis, {C. L.} and Bamidis, {P. D.}", year = "2010", doi = "10.1007/978-3-642-13039-7_13", language = "English", isbn = "978-3-642-13038-0", pages = "49--52", booktitle = "XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010", publisher = "Springer", address = "Germany", } . XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010.

Moridis, C.N., Klados, M.A., Terzis, V., Economides, A.A., Karabatakis, V.E., Karlovasitou, A., Bamidis, P.D.(2010). Affective learning: Empathetic embodied conversational agents to modulate brain oscillations . IFMBE Proceedings. 29. p. 675-678.

Manousos Klados, C. N. Moridis, V. Terzis, A. A. Economides, V. E. Karabatakis, A. Karlovasitou, P. D. Bamidis(2010). Affective learning . XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010, MEDICON 2010. 29. p. 675--678. Springer

Peranonti, E.G., Klados, M.A., Papadelis, C.L., Kontotasiou, D.G., Kourtidou-Papadeli, C., Bamidis, P.D.(2010). Can the EEG indicate the FiO2 flow of a mechanical ventilator in ICU patients with respiratory failure? . IFMBE Proceedings. 29. p. 827-830.

Manousos Klados, E G Peranonti, C L Papadelis, D G Kontotasiou, C Kourtidou-Papadeli, P D Bamidis(2010). Can the EEG Indicate the FiO2 Flow of a Mechanical Ventilator in ICU Patients with Respiratory Failure? . XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010. p. 827--830. Springer

Lithari, C., Klados, M.A., Bamidis, P.D.(2010). Graph analysis on functional connectivity networks during an emotional paradigm . IFMBE Proceedings. 29. p. 115-118.

Manousos Klados, C. Lithari, P. D. Bamidis(2010). Graph Analysis on Functional Connectivity Networks during an Emotional Paradigm . XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010. p. 115--118. Springer

Lithari, C., Frantzidis, C.A., Papadelis, C., Klados, M.A., Pappas, C., Bamidis, P.O.(2010). Small-world properties of brain Functional Connectivity Networks are affected by emotional stimuli . Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine, ITAB.

Klados, M.A., Bratsas, C., Frantzidis, C., Papadelis, C.L., Bamidis, P.D.(2010). A kurtosis-based automatic system using naïve bayesian classifier to identify ICA components contaminated by EOG or ECG artifacts . IFMBE Proceedings. 29. p. 49-52.

Moridis, C.N., Klados, M.A., Kokkinakis, I.A., Terzis, V., Economides, A.A., Karlovasitou, A., Bamidis, P.D., Karabatakis, V.E.(2010). The impact of audio-visual stimulation on alpha brain oscillations: An EEG study . Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine, ITAB.

Frantzidis, C.A., Lithari, C.D., Klados, M.A., Pappas, C., Barnidis, P.D.(2010). Synchronization analysis of short EEG data through time-evolving Relative Wavelet Entropy and IAPS affective visual stimuli . Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine, ITAB.

Manousos Klados, Christos A. Frantzidis, Chrysa D. Lithari, C. Pappas, Panagiotis D. Barnidis(2010). Synchronization analysis of short EEG data through time-evolving Relative Wavelet Entropy and IAPS affective visual stimuli . ITAB 2010 - 10th International Conference on Information Technology and Applications in Biomedicine. IEEE

REG-ICA: A new hybrid method for EOG Artifact Rejection @inproceedings{152a3592ad1a4e90b94556065b143b54, title = "REG-ICA: A new hybrid method for EOG Artifact Rejection", abstract = "The plethora of Artifact Rejection (AR) techniques proposed for removing electrooculographic (EOG) artifacts from electroencephalographic (EEG) signals can be separated into two main categories. The first category is composed of regression - based methods, while the second one consists of Blind Source Separation (BSS) - methods. A major disadvantage of BSS - based methodology is that the artifactual components include also neural activity, thus their rejection leads to the distortion of the underlying cerebral activity. The current study tries to solve the aforementioned problem by proposing a new hybrid algorithm for EOG AR. According to this automatic approach, called REG-ICA, Independent Component Analysis (ICA) is used to decompose EEG signals into spatial independent components (ICs). Then an adaptive filter, based on a stable Version of the Recursive Least Square (sRLS) algorithm, is applied to ICs so as to remove only EOG artifacts and maintain the neural signals intact. Then the cleaned ICs are projected back, reconstructing the artifact - free EEG signals. In order to evaluate the performance of the proposed technique, REG-ICA has been compared with the Least Mean Square (LMS) approach, in simulated EEG data. Two criteria were used for the comparison: how successfully algorithms remove eye blinking artifacts, and how much the EEG signals are distorted. Results support the argument that REG-ICA removes successfully EOG activity, while it minimizes the distortion of the underlying cerebral activity in contrast to LMS.", keywords = "analysis, artifact rejection, biological, eeg, electroculogram, electroencephalogram, eog, ica, independent component, physiological, regression, the most frequently seen", author = "Klados, {Manousos A.} and Papadelis, {Christos L.} and Bamidis, {Panagiotis D.}", year = "2009", month = nov, doi = "10.1109/ITAB.2009.5394295", language = "English", pages = "1--4", booktitle = "2009 9th International Conference on Information Technology and Applications in Biomedicine", publisher = "IEEE", address = "United States", note = "9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009 ; Conference date: 04-11-2009 Through 07-11-2009", } . 2009 9th International Conference on Information Technology and Applications in Biomedicine.

Manousos Klados, C. Papadelis, C. D. Lithari, P. D. Bamidis(2009). The Removal Of Ocular Artifacts From EEG Signals: A Comparison of Performances For Different Methods . 4th European Conference of the International Federation for Medical and Biological Engineering. p. 1259--1263. Springer

Klados, M.A., Papadelis, C.L., Bamidis, P.D.(2009). REG-ICA: A new hybrid method for EOG artifact rejection . Final Program and Abstract Book - 9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009.

Bratsas, C., Frantzidis, C.A., Klados, M., Papadelis, C., Pappas, C., Bamidis, P.D.(2009). Towards a semantic framework for an integrative description of neuroscience patterns and studies: A case for emotion-related data . Studies in Health Technology and Informatics. 150. p. 322-326.

Klados, M.A., Papadelis, C., Lithari, C.D., Bamidis, P.D.(2008). The removal of ocular artifacts from EEG signals: A comparison of performances for different methods . IFMBE Proceedings. 22. p. 1259-1263.

Christos N. Moridis, Manousos A. Klados, Ioannis A. Kokkinakis, Vasileios Terzis, Anastasios A. Economides, Anna Karlovasitou, Panagiotis D. Bamidis, Vasileios E. Karabatakis, 2010, 'The impact of audio-visual stimulation on alpha brain oscillations: An EEG study', <i>Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine</i>, pp. 1-4 .

Manousos A. Klados, Christos L. Papadelis, Panagiotis D. Bamidis, 2009, 'REG-ICA: A new hybrid method for EOG Artifact Rejection', <i>2009 9th International Conference on Information Technology and Applications in Biomedicine</i>, pp. 1-4 .

Christos A. Frantzidis, Chrysa D. Lithari, Manousos A. Klados, C. Pappas, Panagiotis D. Bamidis, 2010, 'Synchronization analysis of short EEG data through time-evolving relative wavelet entropy and IAPS affective visual stimuli', <i>Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine</i>, pp. 1-4 .

CONFERENCE ABSTRACT

Human Connectome as a big-data problem: New approaches for analysis and visualization. @conference{568a1e1090374e9a957d839c1ce4740f, title = "Human Connectome as a big-data problem: New approaches for analysis and visualization.", abstract = "Calculating so the functional as the structural relations between different cerebral areas and align those links to the personal differences in cognitive, behavioral and affective domains seems to be a crucial step for the understanding of human connectome [1]. As the resolution of the accurate brain imaging increases, the number of these links also increases, generating more complex networks, elevating the connectivity research to a big data problem. This alongside with the restricted computational resources indicates the urgent need for developing new models and new approaches in order to solve and visualize this big-data problem. Graph theory is widely used for mathematical modeling of complex networks. According to graph theory, a graph is defined as a pair between a set of vertices and a set of edges. Graphs can be described so by the existence of directionality in their edges (directed/undirected), as by their weight (weighted/unweighted-binary). As Although undirected binary graphs can be used to reduce the complexity of the networks, much of the information is lost due to arbitrary thresholding [2,3] leading to the disconnection syndrome [4], with negative implications so for the computation of several graph theoretical parameters as for the cohesion of the network [5], while there is also an introduction of several biases [6]. One way to reduce the complexity of the network overcoming the aforementioned biases [7] is by modeling our weighted graphs using Minimum Spanning Tree (MST) methodology. A MST is a loopless subgraph of the original graph connecting all nodes [8], and it can be used to reduce the dimensionality of the graph, because by it{\textquoteright}s definition it has n-1 edges, while the initial graph has n*(n-1)/2 edges. The goal of the MST is to minimize the cost (increase the weights) of the graph and preserve only the most important edges [9], and it is assumed to be the backbone of the initial graph. Another approach to reduce the complexity of the brain connectome, is by employing linear or non-linear dimension reduction techniques. One non-linear technique, called diffusion mapping [10], which has recently draw the attention of the neuroscientific community [11], is based on the principles of spectral graph theory. A typical example of the aligned mean diffusion map extracted by the resting state fMRI correlation networks (468 subjects) is depicted in Figure 1. Each node is colored according to it{\textquoteright}s connectivity profile, meaning that two nodes with the same color (red, green, blue, etc.) are connected similarly to the rest of the brain, while they are also strongly interconnected. On the other hand, as far the difference of two nodes in diffusion spectrum increases, the dissimilarity of these nodes also increases, and moreover the transition from one node to another becomes more difficult. Another important application of diffusion mapping is the visualization of functional connectivity in a much easier and more interpretable way. Indeed, a great challenge for neuroscientists today is to visualize connectivity networks incorporating so the functional as the anatomical information, and produce easily interpretable images. Currently the most common approach is the graph-like visualization overlaid in head/cortex. In this context, the nodes bear all the anatomical information by denoting a point in an anatomical space (e.g. MNI, IS 10/20, etc.), so the graph is aligned to head{\textquoteright}s/cortex{\textquoteright}s anatomy. Although this approach seems as a straightforward solution for the visualization of functional networks, it only works with a low number of edges. When the number of edges increases dramatically, this methodology will lead to the obfuscation of the underlying anatomical space [12]. Several other methods have been proposed so far for the illustration of human connectome [12], however still a lot of work should be done in order to visualize dense connectivity matrices. Concluding, we should again remark that as the neuroimaging resolution increases, and the human connectome becomes even more computationally demanding, either in terms of analysis or for visualization purposes, we need to pay more attention in dimensionality reduction techniques, so as to ensure that we maintain the greatest possible portion of the information hidden in our connectome.", author = "Klados, {Manousos A.}", year = "2016", month = oct, day = "9", doi = "10.3389/conf.fnhum.2016.220.00012", language = "English", } .

Mathematical Anxiety influences the cortical connectivity profiles in lower alpha band during working memory tasks @conference{9ca757dcf9f042499f0fe091136117ef, title = "Mathematical Anxiety influences the cortical connectivity profiles in lower alpha band during working memory tasks", abstract = "Introduction Highly math-anxious (HMA) individuals are characterized by a strong tendency to avoid math, which ultimately undercuts their math competence and forecloses important career paths (Ashcraft, 2002). It is hypothesized that worries and intrusive thoughts associated with math anxiety (MA) reduce working memory resources needed for cognitively demanding math tasks (Chang & Beilock, 2016). However, mental processes that access the memory representations of mathematical knowledge has not been fully uncovered (Ashcraft, 2001). Previous studies indicate that the frontal cortex is dominantly involved in working memory (WM) and more specifically while updating the working memory representations (Smith & Jonides, 1997). Additionally, Klados et. al. 2015 show that higher event-related potential (ERP) measures of HMA subjects are predominantly located at frontocentral sites at cortex, while performing WM tasks. Here, we aim to explore the changes in cortical connectivity profile induced by MA during WM tasks. Methods EEG recordings were measured from 32 adults during performance of WM tasks with two levels of difficulty, 1-back (BT1) and 2-back (BT2) (Klados et al., 2015). According to the Abbreviated Math Anxiety Scale (AMAS), half of the participants were selected among the highly math anxious (HMA) students, whereas the other half had low math anxiety (LMA) (Hopko et al., 2003). ERPs were recorded via a Neurofax EEG-1200 system from 57 electrode sites according to a modified international 10/10 system using an Electrocap. Epoch length was 1200 ms including 200 ms prestimulus baseline. Signals were filtered offline between 0.5 and 45 Hz and submitted to an ICA procedure to identify ocular artifact components (Bell & Sejnowski, 1995). These artifact components were filtered with REGICA (Schl{\"o}gl et al., 2007, Klados et al., 2009, 2011). Resulting waveforms were inspected visually and epochs containing visible artifacts in the first 500 ms post-stimulus were removed. To overcome the impact of varying electrical conductivity among head compartments on functional connectivity analyses (Nolte et. al., 200), the cortical activity was estimated from 28 EEG signals by adopting a cortical dipole model (He & Wu, 1999, Mattia et. al., 2009). Here, we referenced MNI152 template as an average head model. Scalp, cortex, outer- and inner-skull were extracted by implementing the Boundary Element Method (BEM) with 302 nodes (Uscedu, 2016). Finally, a column-norm normalization was used to prevent the linear inverse problem. This way, we obtained a transition kernel from 57 scalp signals to 302 cortical signals. We constructed connectivity matrices based on 302 cortical signals by using Magnitude Squared Coherence (MSC) in upper alpha band (8-10 Hz) (Lithari et. al., 2012, Klados et. al., 2013). Then, we embed these connectivity networks to the “connectivity components” by employing a nonlinear dimensional reduction technique (Coifman & Lafon, 2016). The first component captures the highest variance, i.e. the similarity/dissimilarity of the connectivity patterns of source regions. We used 2-way ANOVA with factors MA (HMA & LMA) and task difficulty (BT1 & BT2) along the first connectivity component. The p-values were adapted by the FDR correction (Benjamini & Hochberg, 1995). Results Fig.1 demonstrates the mixed effect of group by task interaction on the connectivity profiles. There is a significant interaction between MA and task difficulty, that is prominent in dorsolateral prefrontal cortex (DLPFC), temporal lobe, left ventromedial PFC, right inferior-parietal lobule (IPL), somatosensory and right motor areas. For each significant region (p(G x T)", keywords = "Math Anxiety, Diffusion Magnetic Resonance Imaging, connectivity, EEG, functional connectivity", author = "{\c S}eyma Bayrak and Daniel Margulies and Panagiotis Bamidis and Klados, {Manousos A.}", year = "2016", month = jul, day = "30", doi = "10.3389/conf.fnhum.2016.220.00001", language = "English", } .

Investigating the correlation between crystallized IQ and network metrics in cerebellum using resting-state fMRI @conference{aed6b55f8d274bf0871b1d4dc0c6b2d1, title = "Investigating the correlation between crystallized IQ and network metrics in cerebellum using resting-state fMRI", abstract = "The network of cerebellum was analyzed in order to investigate its overall organization in individuals with high and low crystallized Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were collected from 150 subjects in resting-state from the Human Connectome Project database [15, 16] and further separated into two categorical groups based on their IQ scores, resulting to 76 low-IQ and 74 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas [2, 3] (lobules I-IV, V, VI, Crus I, Crus II, VIIb, VIIIa, VIIIb, IX, X) as shown in Fig. 1 (cerebellum{\textquoteright}s flat surface representation [3] is also provided where color coding has been applied based on each lobule{\textquoteright}s volumetric size). These lobules serve different functions e.g. cognition [4, 5, 10, 11], emotion [4, 10, 12]. Lobule Vermis Crus I was found to contain less than 0.005% of the total cerebellar volume and thus was removed from further analysis. Thereafter, correlation matrices were constructed by computing the zero-lag temporal Pearson correlation coefficients between the average BOLD time-series for each pair of ROIs. By computing conventional graph metrics, small-world network properties [6, 7, 8, 18] were discovered using the weighted clustering coefficient (averaged over all nodes to define its global version) and characteristic path length (Supplementary Table 1) for estimating the trade-off between segregation and integration. In addition, the connectivity metric was computed for extracting the average cost per network (Supplementary Table 1). In this study, the concept of Minimum Spanning Tree (MST) was adapted and further implemented in order to avoid methodological biases and enable graph comparisons as well as retain only the strongest connections per network (average weighted and undirected graphs per IQ group are illustrated in Fig. 2 with their corresponding MSTs, where the size of each node in the MST linearly depends on its betweenness centrality value) [9, 13, 14]. Subsequently, six global (degree correlation, diameter, leaf fraction, kappa, radius, tree hierarchy) and three local (betweenness centrality, degree, eccentricity) MST metrics were calculated in order to retrieve useful information concerning the functional and structural characteristics of each MST (Supplementary Table 1) [14]. Moreover, the local metrics of degree (DEG) and betweenness centrality (BC) were used to detect hubs, i.e. nodes with high importance, for both IQ groups, as presented in Fig. 3 (where each node{\textquoteright}s size linearly depends on the corresponding BC (A), DEG (B) values, according to the bar plots which represent the percentage of subjects with the highest BC (C), DEG (D) values per ROI). Additionally, the correlations between the median response time (MRT) and the corresponding lobule metrics per IQ group and gender were calculated. Studies in cerebrum have shown that efficiency of networks at rest is higher in more intelligent individuals [1, 15]. Cerebellar lobules have specific (some of them are reciprocal, some not) connections to cerebral sites (mainly frontal and parietal areas) so as to serve cognitive functions in cognitive function [4, 5, 10, 11]. Given the connection of the cerebellum to areas which at rest show high efficiency in high IQ, we are interested to examine whether a similar hypothesis stands for cerebellum. Our findings (Supplementary Table 2) show that: (i) Small-world network organization characterizes cerebellar networks of both men and women at rest state and for low and high-IQ subjects without significant differences. (ii) Maximum correlation between MRT and DEG (r = 0.48, p = 0.001) and BC (r = 0.41, p = 0.005) showed a positive correlation as well as a significant dominance of the Left X lobule in low IQ individuals in the women population. (iii) Higher values in DEG and BC were identified in lobules Left VI, Left Crus I, Right VI, Right Crus I. (iv) There were several differences in local network parameters: lobules Left VI, Left Crus I and Right VI had high local values (DEG & BC) and are hubs, i.e. nodes with high importance, with higher values in individuals with high IQ. (v) More importantly, there are interesting differences between men (low-IQ: 30, high-IQ: 36) and women (low-IQ: 46, high-IQ: 38) as follows. (a) In women there are differences in network parameters among high and low IQ individuals indicative of more robust network organization in cases of higher IQ. (b) Among low-IQ individuals, men network parameters showed more effective organization. Attempting to explain the findings for the lobules with higher correlation with the median response time, we refer to published studies. Left VI lobule is related to motor control, lobules Left Crus I, Right Crus I, Right VI and Right X are related to cognitive and limbic areas of the cerebral hemispheres [4, 5, 10, 11]. It is worth noticing that there are overlapping regions. Nevertheless, the aforementioned differences between men and women, as well as high and low IQ individuals, show more effective organization in high IQ females in relation to low IQ females and more effective organization in males. In summary, to our knowledge this is the first network study of the cerebellum that attempts to assess local and widespread brain-connectivity characteristics in relation to low and high-IQ men and women. Several studies have discovered differences between low and high-IQ subjects in cerebral organization and network metrics, such as differences in several brain regions or connections, small-world network organizations, as well as differences in network parameters and neural efficiency [1, 15]. Nonetheless, future studies are essential in order to explain cerebrum and cerebellar local and widespread findings.", keywords = "Cerebellum, fMRI, small-world network, minimum spanning tree, crystallized IQ, median respone time", author = "Vasileios Pezoulas and Michalis Zervakis and Sifis Micheloyannis and Klados, {Manousos A.}", year = "2016", month = jul, day = "30", doi = "10.3389/conf.fnhum.2016.220.00013", language = "English", } .

Reorganization of brain networks after spinal cord injury: a qualitative synthesis of the literature @conference{5ec659273a664232aaa1b8f9d9513293, title = "Reorganization of brain networks after spinal cord injury: a qualitative synthesis of the literature", abstract = "Introduction: Changes in brain organization and structure have been associated with spinal cord injury (SCI) and have been extensively studied (Freund et al. 2013; Nardone et al. 2013). On the other hand, our understanding of brain connectivity following SCI is significantly lower, with studies appearing during the last decade. In the present study we attempt a qualitative synthesis of relevant peer-reviewed literature. Methodology: We performed a search in Pubmed, Scopus and ScienceDirect using the terms “brain/cortical connectivity/network” and “spinal cord injury”. We further tried to identify relevant articles through retrieved original and review papers. In our synthesis, we included only original studies estimating brain connectivity on SCI patients. We excluded reviews, brain activation and intrinsic spinal cord connectivity studies, theoretic mentions of brain networks and studies on animals (with one exception in the absence of a longitudinal study on humans). Results: Changes in neural function and brain connectivity have been shown to appear even during the early stages of the chronic condition and to correlate with the degree of neurological impairment (Hou et al. 2014a; 2014b). A study on a mixed group of complete/incomplete SCI patients within the first month post-injury with fMRI depicted altered spontaneous resting-state brain activation in almost all cortical and sub-cortical sensorimotor areas (Zhu et al. 2016). An fMRI study on SCI patients at mean two months post-injury showed, not only structural changes such as important gray matter atrophy at the sensorimotor cortical areas and pathways (Hou et al. 2014a) but network alterations as well (Hou et al. 2014b). Decreased inter-hemispheric resting-state functional connectivity (FC) was calculated between sensorimotor cortices bilaterally and increased intra-hemispheric resting-state FC within the sensorimotor cortex, premotor area, supplementary motor area (SMA), as well as within other nodes of the motor pathways such as the thalamus and cerebellum. Another study also demonstrated increased resting-state FC between primary motor areas and SMA or basal ganglia (Min et al. 2015b). This intra-hemispheric increase also correlated with higher degree of motor impairment (Hou et al. 2014b). On the other hand, somatosensory components of the sensorimotor network showed decreased connectivity in SCI patients compared to healthy controls (Min et al. 2015b). Predictors of good versus poor neurological recovery at 6 months post-injury included increased resting-state FC between the primary motor cortex and SMA and premotor cortex for those patients that showed good motor recovery (Hou et al. 2016). Graph properties of resting-state networks, as measured by fMRI in patients with incomplete cervical SCI, did not present significant changes except for greater path lengths compared to healthy individuals (Min et al. 2015a). FC changes appear to be dynamic post-injury procedures. While this has not been directly demonstrated in a human longitudinal study, evidence comes from a study on rhesus monkeys (Rao et al. 2016). Resting-state fMRI recordings at pre-injury and 4, 8 and 12 months post-injury showed initially increased FC between multiple major sensorimotor nodes such as primary sensorimotor cortices, SMA and putamen (Rao et al. 2016), a finding that possibly corresponds to the increased intra-hemispheric FC reported by Hou et al. (2014b) in humans. Gradually FC tended to approach baseline levels in most areas at 12 weeks post-injury. During chronic phases of complete SCI, the continued disruption of sensorimotor pathways causes reorganization of resting-state functional networks, namely an overall decrease in FC (Oni-Orisan et al. 2016). Primary motor and somatosensory areas show decreased FC between them and adjacent cortical sensorimotor nodes both intra- and inter-hemispherically. Deeper connectivity is also altered, as an increase of FC between left primary somatosensory area and bilateral thalami was also shown (Oni-Orisan et al. 2016). Furthermore, the importance of residual reciprocal sensorimotor communication (in an incomplete injury) has been demonstrated in a case of measuring an increase of resting-state FC in fMRI after rehabilitation sessions even a decade post-injury (Chisholm et al. 2015). The brain network properties of chronic complete SCI patients have also been investigated with high-resolution EEG recordings and using graph analysis (Astolfi et al. 2006a; 2006b; De Vico Fallani et al. 2006). Statistical comparison of SCI patients and healthy subjects showed a higher degree of local efficiency of brain networks of SCI patients during attempted (paralyzed) foot movements, suggested to be a compensative mechanism (De Vico Fallani et al. 2007; Sinatra et al. 2009). Cingulate motor area (CMA) was identified as an important information hub and time-varying estimation of connectivity further showed larger cortical networks for the SCI patients and greater involvement of the parietal cortex around motor imagery onset (Astolfi et al. 2007; 2008; 2009; Sinatra et al. 2009). On the other hand, SCI patient brain networks seem to show less communication redundancy in the higher EEG spectra (and supposedly less tolerance for dysfunction) compared to healthy individuals, presenting suppressed longer alternative inter-cortical region pathways, as a negative result of the spinal trauma (De Vico Fallani et al. 2009). In lower spectra (such as theta band), no difference in redundancy was shown and higher degree of communication between closest cortical areas in SCI patient networks (De Vico Fallani et al. 2011). The sensorimotor networks of SCI patients and healthy individuals share similar patterns of connectivity but some new functional interactions were identified as unique to SCI patients (Mattia et al. 2009), namely inflow of information from the ipsilateral CMA and SMA to the superior parietal cortex (SPC) and information exchange between bilateral primary motor foot area and SMA. These occurrences could be attributed to both adaptive and maladaptive organization effects after the injury (Mattia et al. 2009; Sinatra et al. 2009; Astolfi et al. 2010; De Vico Fallani et al. 2010). Conclusion: Large-scale, multi-modal, longitudinal studies on SCI patients are needed to understand how brain network reorganization is established and progresses through the course of the condition. Simultaneous analysis of brain and spinal cord activations and interactions could also shed further light (Vahdat et al. 2015). The expected insight holds great clinical relevance in neurofeedback based neurorehabilitation and the design of connectivity-based Brain-Computer Interfaces for SCI patients (Athanasiou et al. 2016).", keywords = "cortical connectivity, brain connectivity, spinal cord injury, cortical reorganization, functional connectivity, Functional reorganization", author = "Alkinoos Athanasiou and Klados, {Manousos A.} and Nicolas Foroglou and Klados, {Kyriaki Rafailia} and Konstantinos Polyzoidis and Barnidis, {Panagiotis D}", year = "2016", month = jul, day = "30", doi = "10.3389/conf.fnhum.2016.220.00036", language = "English", } .

EDITED BOOK

Biennial Meeting of the Society of Applied Neuroscience (SAN2016) @book{0b041bb0089f41309531edba0b4e4eec, title = "Biennial Meeting of the Society of Applied Neuroscience (SAN2016)", abstract = "This book of abstracts contains the abstracts presented in the SAN2016 conference. SAN was established in Europe as an international society to investigate the potential applications of neuroscience, to promote research on validation and to encourage the multidisciplinary approaches in applied neuroscience. The foundational interest of SAN is brain electrophysiology, seen in the broader view of neuroscientific models of how the brain works neurophysiologically. In particular SAN promotes the translation of these electrophysiology-based models to the clinical practice via feedback techniques, via the application of external electrical or magnetic stimuli, and lately via the application of non-pharmaceutical interventions. The main issues discussed during SAN2016 and they are presented in this book include methodology, modeling, theory, applications, as well as reviews related to: EEG/MEG/fMRI/fNIRS, QEEG, biofeedback, neurofeedback, TMS, DCS, neuronal reorganization, rehabilitation, brain computer interfaces, brain connectivity, event-related potentials, inverse solution, cognition, emotion and aging.", editor = "Klados, {Manousos A.} and Gruzelier, {John H.} and Bamidis, {Panagiotis D.}", year = "2016", month = oct, doi = "10.3389/978-2-88919-993-8", language = "English", isbn = "9782889199938", publisher = "Frontiers Media S.A.", address = "Switzerland", } .

BOOK CHAPTER

State of the Art and Future Prospects of Nanotechnologies in the Field of Brain-Computer Interfaces @inbook{7a0a6a88cf5d451e99b0d71c64a22f39, title = "State of the Art and Future Prospects of Nanotechnologies in the Field of Brain-Computer Interfaces", abstract = "This paper presents a new envisaged micro-rheometer device based on a Lab-on-a-Chip solution, focused to biomedical applications with small volume of sample (less than 50 µL). Based on capturing the fluids velocity along a microchannel, this novel device presents great advantages over products already on the market for measuring the fluid viscosity, ranging from the cost to the simplicity of operation. The presented device has the capability to extract the viscosity of any type of fluid with a microdevice manufactured with PDMS (polydimethylsiloxane) and electrodes screen-printed over a PET (polyethylene terephthalate) surface. In the present work Newtonian fluids, such as water and ethylene glycol at different concentrations, have been used to calibrate the device, and non-Newtonian fluids such as blood has been employed to test it. We have observed in our initial experiments the predictable Newtonian behavior in the case of water and ethylene glycol and with blood, the non-Newtonian nature of the sample. Analyzing the results, the precision and accuracy of the device has been validated obtaining values of viscosity, with the presented set-up, which differ from those in the literature by a 10%.", author = "Alkinoos Athanasiou and Klados, {Manousos A.} and Alexander Astaras and Nicolas Foroglou and Ioannis Magras and Bamidis, {Panagiotis D.}", year = "2016", month = sep, day = "17", doi = "10.1007/978-3-319-32703-7_90", language = "English", isbn = "978-3-319-32701-3", volume = "57", pages = "462--466", editor = "Kyriacou, { E} and {Christofides }, S and C Pattichis", booktitle = "XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016.", publisher = "Springer", address = "Germany", } . XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016.

State of the Art and Future Prospects of Nanotechnologies in the Field of Brain-Computer Interfaces @inbook{7a0a6a88cf5d451e99b0d71c64a22f39, title = "State of the Art and Future Prospects of Nanotechnologies in the Field of Brain-Computer Interfaces", abstract = "This paper presents a new envisaged micro-rheometer device based on a Lab-on-a-Chip solution, focused to biomedical applications with small volume of sample (less than 50 µL). Based on capturing the fluids velocity along a microchannel, this novel device presents great advantages over products already on the market for measuring the fluid viscosity, ranging from the cost to the simplicity of operation. The presented device has the capability to extract the viscosity of any type of fluid with a microdevice manufactured with PDMS (polydimethylsiloxane) and electrodes screen-printed over a PET (polyethylene terephthalate) surface. In the present work Newtonian fluids, such as water and ethylene glycol at different concentrations, have been used to calibrate the device, and non-Newtonian fluids such as blood has been employed to test it. We have observed in our initial experiments the predictable Newtonian behavior in the case of water and ethylene glycol and with blood, the non-Newtonian nature of the sample. Analyzing the results, the precision and accuracy of the device has been validated obtaining values of viscosity, with the presented set-up, which differ from those in the literature by a 10%.", author = "Alkinoos Athanasiou and Klados, {Manousos A.} and Alexander Astaras and Nicolas Foroglou and Ioannis Magras and Bamidis, {Panagiotis D.}", year = "2016", month = sep, day = "17", doi = "10.1007/978-3-319-32703-7_90", language = "English", isbn = "978-3-319-32701-3", volume = "57", pages = "462--466", editor = "Kyriacou, { E} and {Christofides }, S and C Pattichis", booktitle = "XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016.", publisher = "Springer", address = "Germany", } . XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016.

A Modular Architecture of a Computer-Operated Olfactometer for Universal Use @inbook{91567b3ca9ec4f7986a1a44437fb5784, title = "A Modular Architecture of a Computer-Operated Olfactometer for Universal Use", abstract = "Olfactometers are widely used in the study of the chemical senses from a neurophysiological point of view. Although there is a plethora of olfactometer designs, all of them lack of flexibility in modification. In more details they are not able to dynamically increase the number of odors that can be provided simultaneously or they are not capable to use other form of odorous material than the one they{\textquoteright}ve been designed for. In addition to all these the concentration of the stimulus is estimated indirectly through the ratio of the odorized and the odorless air that is delivered to the subject. Taking into account all these, it is understandable that there is an urgent need for an effective olfactometer which will be able to overcome the aforementioned drawbacks of the existing olfactometers. In this scope, the current study comes to introduce a new computer – operated olfactometer. Its novelty lies on the fact that it has a modular architecture with microcontroller units in every module, which can undoubtfully simplify the system{\textquoteright}s modification. On the other hand it can also estimate directly the Volatile Organic Compounds (VOC) with one sensor for every odor, and one sensor for the overall stimulus.", author = "A. Komnidis and E. Konstantinidis and I. Stylianou and Klados, {M. A.} and A. Kalfas and Bamidis, {P. D.}", year = "2010", doi = "10.1007/978-3-642-13039-7_70", language = "English", isbn = "978-3-642-13038-0", series = "XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010", publisher = "Springer", pages = "280--283", booktitle = "XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010", address = "Germany", } . XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010.

Manousos Klados, Panagiotis D. Bamidis, Christos A. Frantzidis, Evdokimos I. Konstantinidis, Andrej Luneski, Chrysa Lithari, Charalambos Bratsas, Christos L. Papadelis, Costas Pappas(2009). An Integrated Approach to Emotion Recognition for Advanced Emotional Intelligence . HCI 2009: Human-Computer Interaction. Ambient, Ubiquitous and Intelligent Interaction. p. 565--574. Springer

JOURNAL ISSUE

Investigating the role of alpha and beta rhythms in functional motor networks @article{117727a527a24a05acbe23dfdcd7260b, title = "Investigating the role of alpha and beta rhythms in functional motor networks", abstract = "It is recognized that lower electroencephalography (EEG) frequencies correspond to distributed brain activity over larger spatial regions than higher frequencies and are associated with coordination. In motor processes it has been suggested that this is not always the case. Our objective was to explore this contradiction. In our study, seven healthy subjects performed four motor tasks (execution and imagery of right hand and foot) under EEG recording. Two cortical source models were defined, model «A» with 16 regions of interest (ROIs) and model «B» with 20 ROIs over the sensorimotor cortex. Functional connectivity was calculated by Directed Transfer Function for alpha and beta rhythm networks. Four graph properties were calculated for each network: characteristic path length (CPL), clustering coefficient (CC), density (D) and small-world-ness (SW). Different network modules and in-degrees of nodes were also calculated and depicted in connectivity maps. Analysis of variance was used to determine statistical significance of observed differences in the network properties between tasks, between rhythms and between ROI models. Consistently on both models, CPL and CC were lower and D was higher in beta rhythm networks. No statistically significant difference was observed for SW between rhythms or for any property between tasks on any model. Comparing the models we observed lower CPL for both rhythms, lower CC in alpha and higher CC in beta when the number of ROIs increased. Also, denser networks with higher SW were correlated with higher number of ROIs. We propose a non-exclusive model where alpha rhythm uses greater wiring costs to engage in local information progression while beta rhythm coordinates the neurophysiological processes in sensorimotor tasks.", keywords = "Brain waves, Electroencephalography, Functional connectivity, Motor imagery, Motor network, Sensorimotor cortex", author = "Alkinoos Athanasiou and Klados, {Manousos A} and Charis Styliadis and Nicolas Foroglou and Konstantinos Polyzoidis and Bamidis, {Panagiotis D}", note = "Copyright {\textcopyright} 2016 IBRO. Published by Elsevier Ltd. All rights reserved.", year = "2016", month = may, day = "27", doi = "10.1016/j.neuroscience.2016.05.044", language = "English", journal = "Neuroscience", issn = "0306-4522", publisher = "Elsevier", } . Neuroscience.

BOOK

Frantzidis, C.A., Diamantoudi, M.D., Grigoriadou, E., Semertzidou, A., Billis, A., Konstantinidis, E., Klados, M.A., Vivas, A.B., Bratsas, C., Tsolaki, M., et al.(2012). A Mahalanobis distance based approach towards the reliable detection of geriatric depression symptoms co-existing with cognitive decline . IFIP Advances in Information and Communication Technology. 382 AICT. (PART 2). p. 16-25.

Bamidis, P.D., Frantzidis, C.A., Konstantinidis, E.I., Luneski, A., Lithari, C., Klados, M.A., Bratsas, C., Papadelis, C.L., Pappas, C.(2009). An integrated approach to emotion recognition for advanced emotional intelligence . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5612 LNCS. (PART 3). p. 565-574.