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Article ; Online: Epileptic tissue localization using graph-based networks in the high frequency oscillation range of intracranial electroencephalography.

Stergiadis, Christos / Kazis, Dimitrios / Klados, Manousos A

2024 Volume 117, Page(s) 28–35

Abstract: Purpose: High frequency oscillations (HFOs) are an emerging biomarker of epilepsy. However, very few studies have investigated the functional connectivity of interictal iEEG signals in the frequency range of HFOs. Here, we study the corresponding ... ...

Abstract	Purpose: High frequency oscillations (HFOs) are an emerging biomarker of epilepsy. However, very few studies have investigated the functional connectivity of interictal iEEG signals in the frequency range of HFOs. Here, we study the corresponding functional networks using graph theory, and we assess their predictive value for automatic electrode classification in a cohort of 20 drug resistant patients. Methods: Coherence-based connectivity analysis was performed on the iEEG recordings, and six different local graph measures were computed in both sub-bands of the HFO frequency range (80-250 Hz and 250-500 Hz). Correlation analysis was implemented between the local graph measures and the ripple and fast ripple rates. Finally, the WEKA software was employed for training and testing different predictive models on the aforementioned local graph measures. Results: The ripple rate was significantly correlated with five out of six local graph measures in the functional network. For fast ripples, their rate was also significantly (but negatively) correlated with most of the local metrics. The results from WEKA showed that the Logistic Regression algorithm was able to classify highly HFO-contaminated electrodes with an accuracy of 82.5 % for ripples and 75.4 % for fast ripples. Conclusion: Functional connectivity networks in the HFO band could represent an alternative to the direct use of distinct HFO events, while also providing important insights about hub epileptic areas that can represent possible surgical targets. Automatic electrode classification through FC-based classifiers can help bypass the burden of manual HFO annotation, providing at the same time similar amount of information about the epileptic tissue.
MeSH term(s)	Humans ; Electrocorticography/methods ; Female ; Male ; Adult ; Drug Resistant Epilepsy/physiopathology ; Drug Resistant Epilepsy/diagnosis ; Brain/physiopathology ; Epilepsy/physiopathology ; Epilepsy/diagnosis ; Young Adult ; Brain Waves/physiology ; Middle Aged ; Adolescent ; Nerve Net/physiopathology ; Signal Processing, Computer-Assisted ; Electroencephalography/methods
Language	English
Publishing date	2024-01-29
Publishing country	England
Document type	Journal Article
ZDB-ID	1137610-7
ISSN	1532-2688 ; 1059-1311
ISSN (online)	1532-2688
ISSN	1059-1311
DOI	10.1016/j.seizure.2024.01.015
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Article ; Online: A Personalized User Authentication System Based on EEG Signals.

Stergiadis, Christos / Kostaridou, Vasiliki-Despoina / Veloudis, Simos / Kazis, Dimitrios / Klados, Manousos A

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 18

Abstract: Conventional biometrics have been employed in high-security user-authentication systems for over 20 years now. However, some of these modalities face low-security issues in common practice. Brainwave-based user authentication has emerged as a promising ... ...

Abstract	Conventional biometrics have been employed in high-security user-authentication systems for over 20 years now. However, some of these modalities face low-security issues in common practice. Brainwave-based user authentication has emerged as a promising alternative method, as it overcomes some of these drawbacks and allows for continuous user authentication. In the present study, we address the problem of individual user variability, by proposing a data-driven Electroencephalography (EEG)-based authentication method. We introduce machine learning techniques, in order to reveal the optimal classification algorithm that best fits the data of each individual user, in a fast and efficient manner. A set of 15 power spectral features (delta, theta, lower alpha, higher alpha, and alpha) is extracted from three EEG channels. The results show that our approach can reliably grant or deny access to the user (mean accuracy of 95.6%), while at the same time poses a viable option for real-time applications, as the total time of the training procedure was kept under one minute.
MeSH term(s)	Algorithms ; Biometry ; Computer Security ; Electroencephalography/methods ; Information Systems
Language	English
Publishing date	2022-09-13
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22186929
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Article: A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques.

Klados, Manousos A / Bamidis, Panagiotis D

Data in brief

2016 Volume 8, Page(s) 1004–1006

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/ ...

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.
Language	English
Publishing date	2016-06-29
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2786545-9
ISSN	2352-3409
ISSN	2352-3409
DOI	10.1016/j.dib.2016.06.032
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Article ; Online: A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques

Manousos A. Klados / Panagiotis D. Bamidis

Data in Brief, Vol 8, Iss , Pp 1004-

2016 Volume 1006

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
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Science (General) ; Q1-390
Subject code	028
Language	English
Publishing date	2016-09-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Double-Step Machine Learning Based Procedure for HFOs Detection and Classification.

Sciaraffa, Nicolina / Klados, Manousos A / Borghini, Gianluca / Di Flumeri, Gianluca / Babiloni, Fabio / Aricò, Pietro

Brain sciences

2020 Volume 10, Issue 4

Abstract: The need for automatic detection and classification of high-frequency oscillations (HFOs) as biomarkers of the epileptogenic tissue is strongly felt in the clinical field. In this context, the employment of artificial intelligence methods could be the ... ...

Abstract	The need for automatic detection and classification of high-frequency oscillations (HFOs) as biomarkers of the epileptogenic tissue is strongly felt in the clinical field. In this context, the employment of artificial intelligence methods could be the missing piece to achieve this goal. This work proposed a double-step procedure based on machine learning algorithms and tested it on an intracranial electroencephalogram (iEEG) dataset available online. The first step aimed to define the optimal length for signal segmentation, allowing for an optimal discrimination of segments with HFO relative to those without. In this case, binary classifiers have been tested on a set of energy features. The second step aimed to classify these segments into ripples, fast ripples and fast ripples occurring during ripples. Results suggest that LDA applied to 10 ms segmentation could provide the highest sensitivity (0.874) and 0.776 specificity for the discrimination of HFOs from no-HFO segments. Regarding the three-class classification, non-linear methods provided the highest values (around 90%) in terms of specificity and sensitivity, significantly different to the other three employed algorithms. Therefore, this machine-learning-based procedure could help clinicians to automatically reduce the quantity of irrelevant data.
Language	English
Publishing date	2020-04-08
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2651993-8
ISSN	2076-3425
ISSN	2076-3425
DOI	10.3390/brainsci10040220
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Automatic Recognition of Personality Profiles Using EEG Functional Connectivity During Emotional Processing.

Klados, Manousos A / Konstantinidi, Panagiota / Dacosta-Aguayo, Rosalia / Kostaridou, Vasiliki-Despoina / Vinciarelli, Alessandro / Zervakis, Michalis

Brain sciences

2020 Volume 10, Issue 5

Abstract: Personality is the characteristic set of an individual'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 ... ...

Abstract	Personality is the characteristic set of an individual'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.
Language	English
Publishing date	2020-05-03
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2651993-8
ISSN	2076-3425
ISSN	2076-3425
DOI	10.3390/brainsci10050278
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Article: Corrigendum: Resting-State Functional Connectivity and Network Analysis of Cerebellum With Respect to IQ and Gender.

Pezoulas, Vasileios C / Zervakis, Michalis / Michelogiannis, Sifis / Klados, Manousos A

Frontiers in human neuroscience

2018 Volume 12, Page(s) 216

Abstract: This corrects the article on p. 189 in vol. 11, PMID: 28491028.]. ...

Abstract	[This corrects the article on p. 189 in vol. 11, PMID: 28491028.].
Language	English
Publishing date	2018-05-28
Publishing country	Switzerland
Document type	Journal Article ; Published Erratum
ZDB-ID	2425477-0
ISSN	1662-5161
ISSN	1662-5161
DOI	10.3389/fnhum.2018.00216
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Article: A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques

Klados, Manousos A. / Bamidis, Panagiotis D.

Data in Brief. 2016 Sept., v. 8

2016

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	brain ; data collection ; electroencephalography ; head ; models
Language	English
Dates of publication	2016-09
Size	p. 1004-1006.
Publishing place	Elsevier Inc.
Document type	Article
ZDB-ID	2786545-9
ISSN	2352-3409
ISSN	2352-3409
DOI	10.1016/j.dib.2016.06.032
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Interactive effects of dopamine transporter genotype and aging on resting-state functional networks.

Baeuchl, Christian / Chen, Hsiang-Yu / Su, Yu-Shiang / Hämmerer, Dorothea / Klados, Manousos A / Li, Shu-Chen

PloS one

2019 Volume 14, Issue 5, Page(s) e0215849

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 ... ...

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.
MeSH term(s)	Adult ; Aged ; Aging/genetics ; Aging/physiology ; Brain/diagnostic imaging ; Brain/metabolism ; Brain/physiology ; Brain Mapping ; Cognition ; Dopamine Plasma Membrane Transport Proteins/genetics ; Female ; Genotype ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Nerve Net/diagnostic imaging ; Nerve Net/metabolism ; Nerve Net/physiology ; Rest/physiology ; Young Adult
Chemical Substances	Dopamine Plasma Membrane Transport Proteins ; SLC6A3 protein, human
Language	English
Publishing date	2019-05-08
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0215849
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to [corrected] IQ and Gender.

Pezoulas, Vasileios C / Zervakis, Michalis / Michelogiannis, Sifis / Klados, Manousos A

Frontiers in human neuroscience

2017 Volume 11, Page(s) 189

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 ... ...

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 fluid [corrected] 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.
Language	English
Publishing date	2017-04-26
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2425477-0
ISSN	1662-5161
ISSN	1662-5161
DOI	10.3389/fnhum.2017.00189
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