Anastasios Bezerianos

• Professor
• Research Professor at Centre for Research and Technology Hellas

Working memory (WM) is a memory system with a limited capacity that can process and store information temporarily in the performing of cognitive tasks. Despite WM is known to be influenced by age, the difficulty of tasks and trained or not from behavior studies, little is known about their relationships from the aspect of the brain functional netwo...

Cardiac arrest (CA) remains the leading cause of coma, and early arousal recovery indicators are needed to allocate critical care resources properly. High-frequency oscillations (HFOs) of somatosensory evoked potentials (SSEPs) have been shown to indicate responsive wakefulness days following CA. Nonetheless, their potential in the acute recovery p...

In recent years, brain functional connectivity (FC) plays an important role in the field of cognitive state monitoring and has attracted the attention of many researchers. In this study, we explored the effects of rest on the brain by designing a two-session experiment with 20 healthy participants. In session one, subjects were required to do four...

Due to the increasing fatal traffic accidents, there are strong desire for more effective and convenient techniques for driving fatigue detection. Here, we propose a unified framework – E-Key to simultaneously perform personal identification (PI) and driving fatigue detection using a convolutional attention neural network (CNN-Attention). The perfo...

Schizophrenia seriously affects the quality of life. To date, both simple (e.g., linear discriminant analysis) and complex (e.g., 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 clas...

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...

Subjects’ interaction is the core of most human activities. This is the reason why a lack of coordination is often the cause of missing goals, more than individual failure. While there are different subjective and objective measures to assess the level of mental effort required by subjects while facing a situation that is getting harder, that is, m...

Cooperation degradation can be seen as one of the main causes of human errors. Poor cooperation could arise from aberrant mental processes, such as mental overload, that negatively affect the user's performance. Using different levels of difficulty in a cooperative task, we combined behavioural, subjective and neurophysiological data with the aim t...

Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic s...

The most consistent finding of creative ideation in the neuroscientific study of creativity is the increment of EEG α power. However, the majority of existing studies focused only on ERP experimental paradigms while only a few analyzed time-related changes of EEG α power patterns during the time unlocked creation of ideas. Here, we designed an expe...

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...

The development of network theory has introduced new approaches to understand the brain as a complex system. Currently the time-variant functional connectivity of brain networks under complex tasks is still being investigated. To explore connectivity during complex cognitive and motor tasks, this study focused on the relevance of small-worldness to...

In the road for network medicine the newly emerged systems-level subpathway-based analysis methods offer new disease genes, drug targets and network-based biomarkers. In parallel, paired miRNA/mRNA expression data enable simultaneously monitoring of the micronome effect upon the signaling pathways. Towards this orientation, we present a methodologi...

The current study aims to look at the difference in coupling of EEG activity of participant pairs while they perform a cooperative, concurrent, independent yet different task at high and low difficulty levels. Participants performed the National Aeronautics and Space Administration (NASA) designed Multi-Attribute Task Battery (MATB-II) task which s...

Brain computer interface (BCI) is a direct communication pathway between the human central nervous system and external devices primarily aiming at restoring damaged functions such as sight, hearing and movement. Although great achievements have been made for the development of reliable BCI systems to assist people with upper-limb disabilities, rese...

A novel perspective of systems biology is the incorporation of pathway structure data along with transcriptomics studies. In parallel, the plethora of high-throughput experimental studies necessitates employment of meta-analysis approaches in order to obtain more biologically consistent results. Towards this orientation we developed a subpathway-ba...

Generally, the training evaluation methods consist in experts supervision and qualitative check of the operator's skills improvement by asking them to perform specific tasks and by verifying the final performance. The aim of this work is to find out if it is possible to obtain quantitative information about the degree of the learning process throug...

From the perspective of Systems Medicine, cardiac aging is re-addressed through large scale diverse omics investigations and more importantly through their integration. Nowadays, the micronome revolutionized our comprehension of the underlying molecular mechanisms and established its role as a player of utmost importance in cardiac development, hyp...

Abstract Towards unraveling the influenza A (H1N1) immunome, this work aims at constructing the murine host response pathway interactome. To accomplish that, an ensemble of dynamic and time-varying Gene Regulatory Network Inference methodologies was recruited to set a confident interactome based on mouse time series transcriptome data (day 1-day 60...

Quantitative analysis of brain lesions and ischemic infarcts is becoming very important due to their association with cardiovascular disease and normal aging. In this paper, we present a semi-supervised segmentation methodology that detects and classifies cerebrovascular disease in multi-channel magnetic resonance (MR) images. The method combines i...

The aim of this study is to detect pathology, such as cerebrovascular disease, in brain images by assuming that the pathology is beyond the expected morphological variability of normal images. However the construction of a statistical model of the inter-subject variability over the whole high resolution image is especially challenging due to large...

In this paper, a microarray image enhancement of cDNA microarray images using a novel two-level denoising technique and automatic addressing is presented. This study shows that robust denoising, based on wavelet decomposition and spot detection, can be used successfully to remove the noise from microarray images. In addition, this paper introduces...

Time frequency representations (TFR) have been assessed with respect to time and frequency resolution using a simulated and real heart rate variability data, HRV. The Wigner-Ville Distribution proved to have a difficult interpretation due to the presence of the cross-terms. Choi-Williams, CW, and Signal-dependent radially Gaussian kernel, SD, TFRs...

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. This paper proposes a novel Bayesian-based algorithm within the framework of wavelet analysis, which reduces speckle in SAR images while preserving the structural features and textural info...