Performance of game sessions in VR vs standard 2D monitor environment. an EEG study.

Nowadays studies using Virtual Reality (VR) are gaining high popularity due to VR being a better approximation of the ecological environment for visual experiments than standard 2D display settings. VR technology has been already applied in medicine in the therapy of mental disorders, neurorehabilitation, and neurofeedback. However, its effectiveness compared to the standard 2D procedure is still not fully documented and limited information about the neurophysiological underpinnings of VR is provided.

Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder hallmarked by challenges in social communication, limited interests, and repetitive, stereotyped movements and behaviors. Numerous research efforts have indicated that individuals with ASD exhibit distinct brain connectivity patterns compared to control groups. However, these investigations, often constrained by small sample sizes, have led to inconsistent results, suggesting both heightened and diminished long-range connectivity within ASD populations.

Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results.

Most studies on EEG-based biometry recognition report results based on signal databases, with a limited number of recorded EEG sessions using the same single EEG recording for both training and testing a proposed model. However, the EEG signal is highly vulnerable to interferences, electrode placement, and temporary conditions, which can lead to overestimated assessments of the considered methods. Our study examined how different numbers of distinct recording sessions used as training sessions would affect EEG-based verification.

Decoding working memory-related information from repeated psychophysiological EEG experiments using convolutional and contrastive neural networks.

Extracting reliable information from electroencephalogram (EEG) is difficult because the low signal-to-noise ratio and significant intersubject variability seriously hinder statistical analyses. However, recent advances in explainable machine learning open a new strategy to address this problem.The current study evaluates this approach using results from the classification and decoding of electrical brain activity associated with information retention.

Impact of EEG Frequency Bands and Data Separation on the Performance of Person Verification Employing Neural Networks.

The paper is devoted to the study of EEG-based people verification. Analyzed solutions employed shallow artificial neural networks using spectral EEG features as input representation. We investigated the impact of the features derived from different frequency bands and their combination on verification results.

Stronger connectivity and higher extraversion protect against stress-related deterioration of cognitive functions.

Here we attempted to define the relationship between: EEG activity, personality and coping during lockdown. We were in a unique situation since the COVID-19 outbreak interrupted our independent longitudinal study. We already collected a significant amount of data before lockdown.

Spectral analysis versus signal complexity methods for assessing attention related activity in human EEG.

We aimed to find the most effective analytical method for assessment of attention related activity to be used in neurofeedback training. We compared commonly used spectral EEG methods with those measuring signal complexity - based on calculation of entropy and fractal dimension. The 14 subjects were examined with a modified delayed matching-to-sample task.

Complex Modulation Method for Measuring Cross-Frequency Coupling of Neural Oscillations.

There is growing evidence from human intracranial electrocorticography (ECoG) studies that interactions between cortical frequencies are important for sensory perception, cognition and inter-regional neuronal communication. Recent studies have focused mainly on the strength of phase-amplitude coupling in cross-frequency interactions. Here, we introduce a complex modulation method based on measures of coherence to investigate cross-frequency coupling in the neural time series.

Multi-Regional Adaptation in Human Auditory Association Cortex.

In auditory cortex, neural responses decrease with stimulus repetition, known as adaptation. Adaptation is thought to facilitate detection of novel sounds and improve perception in noisy environments. Although it is well established that adaptation occurs in primary auditory cortex, it is not known whether adaptation also occurs in higher auditory areas involved in processing complex sounds, such as speech.

Cross-frequency coupling during auditory perception in human cortex.

Cross-frequency coupling plays an important role in coordinating neuronal computations underlying human perception, learning and memory. Here we compared four methods for measuring phase/amplitude coupling (PAC) of theta (4-7 Hz) and high-gamma (70-150 Hz) in intracranial electrocorticographic (ECoG) recordings. Time-frequency spectral and time-domain evoked responses were derived for comparison.

Spindles in Svarog: framework and software for parametrization of EEG transients.

We present a complete framework for time-frequency parametrization of EEG transients, based upon matching pursuit (MP) decomposition, applied to the detection of sleep spindles. Ranges of spindles duration (>0.5 s) and frequency (11-16 Hz) are taken directly from their standard definitions.

Identification of seizure onset zone and preictal state based on characteristics of high frequency oscillations.

Objective: We investigate the relevance of high frequency oscillations (HFO) for biomarkers of epileptogenic tissue and indicators of preictal state before complex partial seizures in humans.

Methods: We introduce a novel automated HFO detection method based on the amplitude and features of the HFO events. We examined intracranial recordings from 33 patients and compared HFO rates and characteristics between channels within and outside the seizure onset zone (SOZ).

Electroencephalographic profiles for differentiation of disorders of consciousness.

Background: Electroencephalography (EEG) is best suited for long-term monitoring of brain functions in patients with disorders of consciousness (DOC). Mathematical tools are needed to facilitate efficient interpretation of long-duration sleep-wake EEG recordings.

Methods: Starting with matching pursuit (MP) decomposition, we automatically detect and parametrize sleep spindles, slow wave activity, K-complexes and alpha, beta and theta waves present in EEG recordings, and automatically construct profiles of their time evolution, relevant to the assessment of residual brain function in patients with DOC.

Interictal networks in magnetoencephalography.

Epileptic networks involve complex relationships across several brain areas. Such networks have been shown on intracerebral EEG (stereotaxic EEG, SEEG), an invasive technique. Magnetoencephalography (MEG) is a noninvasive tool, which was recently proven to be efficient for localizing the generators of epileptiform discharges.

Statistical analysis of sleep spindle occurrences.

Spindles - a hallmark of stage II sleep - are a transient oscillatory phenomenon in the EEG believed to reflect thalamocortical activity contributing to unresponsiveness during sleep. Currently spindles are often classified into two classes: fast spindles, with a frequency of around 14 Hz, occurring in the centro-parietal region; and slow spindles, with a frequency of around 12 Hz, prevalent in the frontal region. Here we aim to establish whether the spindle generation process also exhibits spatial heterogeneity.

Fully parametric sleep staging compatible with the classical criteria.

We present an open system for sleep staging, based explicitly on the criteria used in visual EEG analysis. Slow waves, theta and alpha waves, sleep spindles and K-complexes are parameterized in terms of time duration, amplitude, and frequency of each waveform by means of the matching pursuit algorithm. It allows the detection of these structures using mostly the criteria from visual EEG analysis.

Event-related desynchronization and synchronization in evoked K-complexes.

K-complexes - phenomena occurring in sleep EEG - pose severe challenges in terms of detection as well as finding their physiological origin. In this study, K-complexes (KCs) were evoked by auditory stimuli delivered during sleep. The use of evoked KCs enables testing the sleeping nervous system under good experimental control.

Explicit parameterization of sleep EEG transients.

Adaptive time-frequency approximations, implemented via the matching pursuit algorithm, offer description of local signals structures in terms of their time occurrence and width, frequency and amplitude. This allows to construct explicit filters for finding EEG waveforms, known from the visual analysis, in the matching pursuit decomposition of signals. In such a way detectors of relevant structures of both transient and oscillatory nature can be constructed in the space of physically meaningful parameters.

High resolution parametric description of slow wave sleep.

We propose a new framework for quantitative analysis of sleep EEG, compatible with the traditional analysis, based upon adaptive time-frequency approximation of signals. Using a high resolution description of EEG rhythms and transients in terms of their time occurrence and width, frequency and amplitude, we present a detailed detection and parameterization of delta waves, including also the time occupied by each delta wave-a parameter inaccessible directly by previously applied signal processing methods. To validate the proposed parameterization, we construct a simple detector of sleep stages 3 and 4, based explicitly upon the classical criteria related to delta waves.