Anastasios Bezerianos - Profile on Academia.edu (original) (raw)

Papers by Anastasios Bezerianos

arXiv (Cornell University), Jul 11, 2020

This tutorial paper refers to the use of graph-theoretic concepts for analyzing brain signals. Fo... more This tutorial paper refers to the use of graph-theoretic concepts for analyzing brain signals. For didactic purposes it splits into two parts: "theory" and "application". In the first part, we commence by introducing some basic elements from graph theory and stemming algorithmic tools, which can be employed for data-analytic purposes. Next, we describe how these concepts are adapted for handling evolving connectivity and gaining insights into network reorganization. Finally, the notion of signals residing on a given graph is introduced and elements from the emerging field of graph signal processing (GSP) are provided. The second part serves as a pragmatic demonstration of the tools and techniques described earlier. It is based on analyzing a multi-trial dataset containing single-trial responses from a visual ERP paradigm. The paper ends with a brief outline of the most recent trends in graph theory that are about to shape brain signal processing in the near future and a more general discussion on the relevance of graph-theoretic methodologies for analyzing continuous-mode neural recordings.

Microcirculation, Nov 9, 2009

To study the effect of myoendothelial communication on vascular reactivity, we integrated detaile... more To study the effect of myoendothelial communication on vascular reactivity, we integrated detailed mathematical models of Ca 2+ dynamics and membrane electrophysiology in arteriolar smooth muscle (SMC) and endothelial (EC) cells. Cells are coupled through the exchange of Ca 2+ , Cl -, K + , and Na + ions, inositol 1,4,5-triphosphate (IP 3 ), and the paracrine diffusion of nitric oxide (NO). EC stimulation reduces intracellular Ca 2+ ([Ca 2+ ] i ) in the SMC by transmitting a hyperpolarizing current carried primarily by K + . The NO-independent endothelium-derived hyperpolarization was abolished in a synergistic-like manner by inhibition of EC SK Ca and IK Ca channels. During NE stimulation, IP 3 diffusing from the SMC induces EC Ca 2+ release, which, in turn, moderates SMC depolarization and [Ca 2+ ] i elevation. On the contrary, SMC [Ca 2+ ] i was not affected by EC-derived IP 3 . Myoendothelial Ca 2+ fluxes had no effect in either cell. The EC exerts a stabilizing effect on calcium-induced calcium release-dependent SMC Ca 2+ oscillations by increasing the norepinephrine concentration window for oscillations. We conclude that a model based on independent data for subcellular components can capture major features of the integrated vessel behavior. This study provides a tissue-specific approach for analyzing complex signaling mechanisms in the vasculature.

The study of working memory (WM) is a hot topic in recent years and accumulating literatures unde... more The study of working memory (WM) is a hot topic in recent years and accumulating literatures underlying the achievement and neural mechanism of WM. However, the effect of WM training on cognitive functions were rarely studied. In this study, nineteen healthy young subjects participated in a longitudinal design with one week N-back training (N=1,2,3,4). Experimental results demonstrated that training procedure could help the subjects master more complex psychological tasks when comparing the pre-training performance with those post-training. More specifically, the behavior accuracy increased from 68.14±9.34%, 45.09±14.90%, 39.12±12.71%, and 32.11±10.98% for 1-back, 2-back, 3-back and 4-back respectively to 73.52±4.01%, 69.14±5.28%, 69.09±6.41% and 64.41±5.12% after training. Furthermore, we applied electroencephalogram (EEG) power and functional connectivity to reveal the neural mechanisms of this beneficial effect and found that the EEG power of δ, θ and α band located in the left temporal and occipital lobe increased significantly. Meanwhile, the functional connectivity strength also increased obviously in δ and θ band. In sum, we showed positive effect of WM training on psychological performance and explored the neural mechanisms. Our findings may have the implications for enhancing the performance of participants who are prone to cognitive.

IEEE Access, 2021

Brain-computer interface (BCI) is a novel human-computer interaction model, which does not depend... more Brain-computer interface (BCI) is a novel human-computer interaction model, which does not depend on the conventional output pathway (peripheral nerve and muscle tissue). In the past three decades, it has attracted the interest of researchers and gradually become a research hotspot. As a typical BCI application, the brain-controlled wheelchair (BCW) could provide a new communicating channel with the external environment for physically disabled people. However, the main challenge of BCW is how to decode multi-degree of freedom control instruction from electroencephalogram (EEG) as soon as possible. The research progress of BCW has been developed rapidly over the past fifteen years. In this review, we investigate the BCW from multiple perspectives, include the type of signal acquisition, the pattern of commands for the control system and the working mechanism of the control system. Furthermore, we summarize the development trend of BCW based on the previous investigation, and it is mainly manifested in three aspects: from a wet electrode to dry electrode, from single-mode to multi-mode, and from synchronous control to asynchronous control. With the continuous development of BCW, we also find new functions have been introduced into BCW to increase its stability and robustness. It is believed that BCW will be able to enter the real-life from the laboratory and will be widely used in rehabilitation medicine in the future.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022

Reliability investigation of measures is impor-1 tant in studies of brain science and neuroengine... more Reliability investigation of measures is impor-1 tant in studies of brain science and neuroengineering. 2 Measures' reliability hasn't been investigated across brain 3 states, leaving unknown how reliable the measures are in 4 the context of the change from alert state to fatigue state 5 during driving. To compensate for the lack, we performed 6 a comprehensive investigation. A two-session experiment 7 with an interval of approximately one week was designed 8 to evaluate the reliability of the measures at both sensor 9 and source levels. The results showed that the average 10 intraclass correlation coefficients (ICCs) of the measures 11 at the sensor level were generally higher than those at the 12 source level, except for the directed between-region mea-13 sures. Single-region measures generally exhibited higher 14 average ICCs relative to between-region measures. The 15 exploration of brain network topology showed that nodal 16 metrics displayed highly varying ICCs across regions and 17 global metrics varied associated with nodal metrics. Single-18 region measures displayed higher ICCs in the frontal and 19 occipital regions while the between-region measures exhib-20 ited higher ICCs in the area involving frontal, central and 21 occipital regions. This study provides an appraisal for the 22 measures' reliability over a long interval, which is informa-23 tive for measure selection in practical mental monitoring. 24

Frontiers in Aging Neuroscience

ObjectivesMeditation imparts relaxation and constitutes an important non-pharmacological interven... more ObjectivesMeditation imparts relaxation and constitutes an important non-pharmacological intervention for people with cognitive impairment. Moreover, EEG has been widely used as a tool for detecting brain changes even at the early stages of Alzheimer’s Disease (AD). The current study investigates the effect of meditation practices on the human brain across the AD spectrum by using a novel portable EEG headband in a smart-home environment.MethodsForty (40) people (13 Healthy Controls—HC, 14 with Subjective Cognitive Decline—SCD and 13 with Mild Cognitive Impairment—MCI) participated practicing Mindfulness Based Stress Reduction (Session 2-MBSR) and a novel adaptation of the Kirtan Kriya meditation to the Greek culture setting (Session 3-KK), while a Resting State (RS) condition was undertaken at baseline and follow-up (Session 1—RS Baseline and Session 4—RS Follow-Up). The signals were recorded by using the Muse EEG device and brain waves were computed (alpha, theta, gamma, and beta)...

Neuroscience of Cognitive Functions: From Theory to Applications

Handbook of Neuroengineering, 2023

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Unobtrusive mental state monitoring based on neurosphysiological signals has seen thriving develo... more Unobtrusive mental state monitoring based on neurosphysiological signals has seen thriving developments over the past decade, with a wide area of applications, from rehabilitation to neuroergonomics and neuromarketing. Particularly, electroencephalography (EEG) and electrooculography (EOG) have been popular techniques to obtain cognitiverelevant biosignals. However, current wearable systems may still pose practical inconvenience, motivating further interest to integrate EOG+EEG recording into streamlined frontal-only sensor montages with sufficient signal fidelity. We propose, here, a spatial filtering approach to reliably extract EOG signals from a reduced set of frontal EEG electrodes, placed on non-hair-bearing (NHB) areas. Within a common signal analytic framework, two distinct schemes are examined. The one is based on standard linear least squares (LLS) and the other on Least Absolute Shrinkage and Selection Operator (LASSO). Both schemes are data-driven techniques, require a small amount of training data, and lead to reliable estimators of EOG activity from EEG signals. The LASSO-based technique, in addition, provides guidelines that generalize well across subjects. Using experimental data, we provide some empirical evidence that our estimators can replace the actual EOG signals in algorithmic pipelines that automatically detect oculographic events, like blinks and saccades.

Progress of Brain Network Studies on Anesthesia and Consciousness: Framework and Clinical Applications

Engineering, 2021

IEEE Transactions on Cybernetics, 2020

Objective: Schizophrenia seriously affects the quality of life. To date, both simple (linear disc... more Objective: Schizophrenia seriously affects the quality of life. To date, both simple (linear discriminant analysis) and complex (deep neural network) machine learning methods have been utilized to identify schizophrenia based on functional connectivity features. The existing simple methods need two separate steps (i.e., feature extraction and classification) to achieve the identification, which disables simultaneous tuning for the best feature extraction and classifier training. The complex methods integrate two steps and can be simultaneously tuned to achieve optimal performance, but these methods require a much larger amount of data for model training. Methods: To overcome the aforementioned drawbacks, we proposed a multi-kernel capsule network (MKCapsnet), which was developed by considering the brain anatomical structure. Kernels were set to match with partition sizes of brain anatomical structure in order to capture interregional connectivities at the varying scales. With the inspiration of widely-used dropout strategy in deep learning, we developed vector dropout in the capsule layer to prevent overfitting of the model. Results: The comparison results showed that the proposed method outperformed the state-of-the-art methods. Besides, we compared performances using different parameters and illustrated the routing process to reveal characteristics of the proposed method. Conclusion:

Graph Theory for Brain Signal Processing

Handbook of Neuroengineering, 2021

Weighted Gate Layer Autoencoders

IEEE Transactions on Cybernetics, 2021

A single dataset could hide a significant number of relationships among its feature set. Learning... more A single dataset could hide a significant number of relationships among its feature set. Learning these relationships simultaneously avoids the time complexity associated with running the learning algorithm for every possible relationship, and affords the learner with an ability to recover missing data and substitute erroneous ones by using available data. In our previous research, we introduced the gate-layer autoencoders (GLAEs), which offer an architecture that enables a single model to approximate multiple relationships simultaneously. GLAE controls what an autoencoder learns in a time series by switching on and off certain input gates, thus, allowing and disallowing the data to flow through the network to increase network\textquoteright s robustness. However, GLAE is limited to binary gates. In this article, we generalize the architecture to weighted gate layer autoencoders (WGLAE) through the addition of a weight layer to update the error according to which variables are more critical and to encourage the network to learn these variables. This new weight layer can also be used as an output gate and uses additional control parameters to afford the network with abilities to represent different models that can learn through gating the inputs. We compare the architecture against similar architectures in the literature and demonstrate that the proposed architecture produces more robust autoencoders with the ability to reconstruct both incomplete synthetic and real data with high accuracy.

arXiv (Cornell University), May 13, 2020

Cognitive states are involving in our daily life, which motivates us to explore them and understa... more Cognitive states are involving in our daily life, which motivates us to explore them and understand them by a vast variety of perspectives. Among these perspectives, brain connectivity is increasingly receiving attention in recent years. It is the right time to summarize the past achievements, serving as a cornerstone for the upcoming progress in the field. In this chapter, the definition of the cognitive state is first given and the cognitive states that are frequently investigated are then outlined. The introduction of the methods for estimating connectivity strength and graph theoretical metrics is followed. Subsequently, each cognitive state is separately described and the progress in cognitive state investigation is summarized, including analysis, understanding, and decoding. We concentrate on the literature ascertaining macro-scale representations of cognitive states from the perspective of brain connectivity and give an overview of achievements related to cognitive states to date, especially within the past ten years. The discussions and future prospects are stated at the end of the chapter.

IEEE 23rd Annual International Conference of the Engineering in Medicine and Biology Society

International Conference of the IEEE Engineering in Medicine and Biology Society, Oct 23, 2001

Comparing Community Detection Algorithms on Neuroimaging Data from Multiple Subjects

It is well-known that the brain is a complex network""brain areas dedicated to differen... more It is well-known that the brain is a complex network""brain areas dedicated to different functions. As such,""consisting of""it is natural to shift toward brain network from brain mapping for deeper understanding of brain functions. Although graph theoretical network metrics measuring global or local properties of network topology have been used to investigate the brain network, they do no provide any information about intermediate scale of the brain network, which is provided by the community structure analysis.""In this paper, we propose a method to compare different community detection algorithms for multiple subjects data in terms of the agreement of a group-based community structure with individual community structures. As it is crucial to find a single group-based community structure for a group of subjects to discuss about brain areas and connections, a number of algorithms based on different approaches have been proposed. To show the feasibility of the method for comparing different algorithms, two community detection algorithms based on different approaches ("virtual-typical-subject" and "group analysis") were examined. The Normalized Mutual Information was computed to measure similarity between the group-based community structure and individual community structures derived from resting-state fMRI functional network, and was used for comparing the two algorithms. Our method demonstrated that the algorithm based on the group-analysis approach detected a group-based community structure with greater agreement with individual community structures.

EEG Functional Connectivity Predicts Individual Behavioural Impairment During Mental Fatigue

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Sep 1, 2020

Mental fatigue deteriorates ability to perform daily activities − known as time-on-task (TOT) eff... more Mental fatigue deteriorates ability to perform daily activities − known as time-on-task (TOT) effect and becomes a common complaint in contemporary society. However, an applicable technique for fatigue detection/prediction is hindered due to substantial inter-subject differences in behavioural impairment and brain activity. Here, we developed a fully cross-validated, data-driven analysis framework incorporating multivariate regression model to explore the feasibility of utilizing functional connectivity (FC) to predict the fatigue-related behavioural impairment at individual level. EEG was recorded from 40 healthy adults as they performed a 30-min high-demanding sustained attention task. FC were constructed in different frequency bands using three widely-adopted methods (including coherence, phase log index (PLI), and partial directed coherence (PDC)) and contrasted between the most vigilant and fatigued states. The differences of individual FC (diff (FC)) were considered as features; whereas the TOT slop across the course of task and the differences of reaction time ( Delta\Delta Delta RT) between the most vigilant and fatigued states were chosen to represent behavioural impairments. Behaviourally, we found substantial inter-subject differences of impairments. Furthermore, we achieved significantly high accuracies for individualized prediction of behavioural impairments using diff(PDC). The identified top diff(PDC) features contributing to the individualized predictions were found mainly in theta and alpha bands. Further interrogation of diff(PDC) features revealed distinct patterns between the TOT slop and Delta\Delta Delta RT prediction models, highlighting the complex neural mechanisms of mental fatigue. Overall, the current findings extended conventional brain-behavioural correlation analysis to individualized prediction of fatigue-related behavioural impairments, thereby moving a step forward towards development of applicable techniques for quantitative fatigue monitoring in real-world scenarios.

IEEE Transactions on Cognitive and Developmental Systems, Jun 1, 2020

Mental fatigue in drivers is one of the leading causes that give rise to traffic accidents. Elect... more Mental fatigue in drivers is one of the leading causes that give rise to traffic accidents. Electroencephalography (EEG) based driving fatigue studies showed promising performance in fatigue monitoring. However, complex methodologies are not suitable for practical implementation. In our simulation based setup that retained the constraints of real driving, we took a step closer to fatigue estimation in a practical scenario. We adopted a pre-processing pipeline with low computational complexity, which can be easily and practically implemented in real-time. Moreover, regression-based continuous fatigue estimation was achieved using power spectral features in conjunction with time as the fatigue label. We sought to compare three regression models and three time windows to demonstrate their effects on the performance of fatigue estimation. Dynamic time warping was proposed as a new measure for evaluating the performance of fatigue estimation. The results derived from the validation of the proposed framework on 19 subjects showed that our proposed framework was promising towards practical implementation. Fatigue estimation by the support vector regression with radial basis function kernel and 5-second window length achieved the best performance. We also provided a comprehensive analysis on the spatial distribution of channels and frequency bands mostly contributing to fatigue estimation, which can inform the feature and channel reduction for real-time fatigue monitoring in practical driving. After reducing the number of electrodes by 75%, the proposed framework retained comparable performance in fatigue estimation. This study demonstrates the feasibility and adaptability of our proposed framework in practical implementation of mental fatigue estimation.

EEG-Based Classification of Olfactory Response to Pleasant Stimuli

Olfactory perception involves complex processing distributed along several cortical and sub-corti... more Olfactory perception involves complex processing distributed along several cortical and sub-cortical regions in the brain. Although several studies have shown that the power spectra of the electroencephalography (EEG) contain information that can be used to differentiate between pleasant and unpleasant stimuli, there are still no studies which investigate whether EEG can be used to differentiate between the neural responses to olfactory stimuli of different levels of pleasantness. For this purpose, in the present study, local brain information within established frequency bands (θ, α and γ) has been used to devise discriminative features in a classification approach. A comparative study of four widely used classifiers is presented and SVM gives the best performance (accuracy = 75.71%). The results reveal that is it possible to objectively discriminate using EEG spectral features between fine levels of perceived pleasantness using the SVM-based classifier within a cross-validation procedure.

Applied Network Science, Aug 16, 2016

The brain is a complex system consisting of regions dedicated to different brain functions, and h... more The brain is a complex system consisting of regions dedicated to different brain functions, and higher cognitive functions are realized via information flow between distant brain areas communicating with each other. As such, it is natural to shift towards brain network analysis from mapping of brain functions, for deeper understanding of the brain system. The graph theoretical network metrics measure global or local properties of network topology, but they do not provide any information about the intermediate scale of the network. Community structure analysis is a useful approach to investigate the mesoscale organization of brain network. However, the community detection schemes are yet to be established. In this paper, we propose a method to compare different community detection schemes for neuroimaging data from multiple subjects. To the best of our knowledge, our method is the first attempt to evaluate community detection from multiple-subject data without "ground truth" community and any assumptions about the original network features. To show its feasibility, three community detection algorithms and three different brain atlases were examined using resting-state fMRI functional networks. As it is crucial to find a single group-based community structure as a representative for a group of subjects to allow discussion about brain areas and connections in different conditions on common ground, a number of community detection schemes based on different approaches have been proposed. A non-parametric permutation test on similarity between group-based community structures and individual community structures was used to determine which algorithm or atlas provided the best representative structure of the group. The Normalized Mutual Information (NMI) was computed to measure the similarity between the community structures. We also discuss further issues on community detection using the proposed method.

Vibration Analysis of Fracture Healing Under Plaster Cast