In recent years, deep-learning models gained attention for electroencephalography (EEG) classification tasks due to their excellent performance and ability extract complex features from raw data. particular, convolutional neural networks (CNN) showed adequate results in brain-computer interfaces (BCI) based on different control signals, including event-related potentials (ERP). this study, we propose a novel CNN, called EEG-Inception, that improves the accuracy calibration time of assistive...

This study aims at assessing the usefulness of deep learning to enhance diagnostic ability oximetry in context automated detection pediatric obstructive sleep apnea (OSA). A total 3196 blood oxygen saturation (SpO2) signals from children were used for this purpose. convolutional neural network (CNN) architecture was trained using 20-min SpO2 segments training set (859 subjects) estimate number apneic events. CNN hyperparameters tuned Bayesian optimization validation (1402 subjects). model...

In this study, we present a new Deep Learning (DL) architecture for Motor Imagery (MI) based Brain Computer Interfaces (BCIs) called EEGSym. Our implementation aims to improve previous state-of-the-art performances on MI classification by overcoming inter-subject variability and reducing BCI inefficiency, which has been estimated affect 10-50% of the population. This convolutional neural network includes use inception modules, residual connections design that introduces symmetry brain...

Neurotechnologies have great potential to transform our society in ways that are yet be uncovered. The rate of development this field has increased significantly recent years, but there still barriers need overcome before bringing neurotechnologies the general public. One these is difficulty performing experiments require complex software, such as brain-computer interfaces (BCI) or cognitive neuroscience experiments. Current platforms limitations terms functionality and flexibility meet...

There is a lack of multi-session P300 datasets for Brain-Computer Interfaces (BCI). Publicly available are usually limited by small number participants with few BCI sessions. In this sense, the large, comprehensive various individuals and multiple sessions has advances in development more effective data processing analysis methods systems. This particularly evident to explore feasibility deep learning that require large datasets. Here we present BCIAUT-P300 dataset, containing 15 autism...

Code-modulated visual evoked potentials (c-VEPs) have marked a milestone in the scientific literature due to their ability achieve reliable, high-speed brain–computer interfaces (BCIs) for communication and control. Generally, these expert systems rely on encoding each command with shifted versions of binary pseudorandom sequences, i.e., flashing black white targets according code. Despite excellent results terms accuracy selection time, high-contrast stimuli cause eyestrain some users. In...

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition that affects cognitive, academic, behavioral, emotional, and social functioning, primarily in children. Despite its high prevalence, current pharmacological treatments are not effective 30% of cases show poor long-term adherence. Non-pharmacological interventions can complement medication-based to improve results. Among these therapies, neurofeedback (NFB) respiratory biofeedback (R-BFB) have shown promise...

Motor imagery (MI) based brain-computer interfaces (BCIs) are widely used in rehabilitation due to the close relationship that exists between MI and motor execution (ME). However, underlying brain mechanisms of remain not well understood. Most MI-BCIs use sensorimotor rhythms elicited primary cortex (M1) somatosensory (S1), which consist an event-related desynchronization followed by synchronization. Consequently, this has resulted systems only record signals around M1 S1. could involve a...

Many brain–computer interface (BCI) studies overlook the channel optimization due to its inherent complexity. However, a careful selection increases performance and users' comfort while reducing cost of system. Evolutionary meta-heuristics, which have demonstrated their usefulness in solving complex problems, not been fully exploited yet this context. The purpose study is two-fold: (1) propose novel algorithm find an optimal set for each user compare it with other existing meta-heuristics;...

Brain-computer interfaces (BCI) based on event-related potentials (ERP) are a promising technology for alternative and augmented communication in an assistive context. However, most approaches to date synchronous, requiring the intervention of supervisor when user wishes turn his attention away from BCI system. In order bring these BCIs into real-life applications, robust asynchronous control system is required through monitoring attention. Despite great importance this limitation, which...

Brain-computer interface (BCI) spellers based on event related potentials (ERPs) are intrinsically synchronous systems. Therefore, selections constantly made, even when users not paying attention to the stimuli. This poses a major limitation in real-life applications, which an asynchronous control is required. The aim of this study design, develop and test novel method discriminate whether user controlling system (i.e., state) or engaged other task non-control state). To achieve such...

Brain-computer interfaces (BCI) have traditionally worked using synchronous paradigms. In recent years, much effort has been put into reaching asynchronous management, providing users with the ability to decide when a command should be selected. However, best of our knowledge, entropy metrics not yet explored. The present study twofold purpose: (i) characterize both control and non-control states by examining regularity electroencephalography (EEG) signals; (ii) assess efficacy scaled...

Video games are crucial to the entertainment industry, nonetheless they can be challenging access for those with severe motor disabilities. Brain-computer interfaces (BCI) systems have potential help these individuals by allowing them control video using their brain signals. Furthermore, multiplayer BCI-based may provide valuable insights into how competitiveness or motivation affects of interfaces. Despite recent advancement in development code-modulated visual evoked potentials (c-VEPs) as...

Neurofeedback training (NFT) has shown promising results in recent years as a tool to address the effects of age-related cognitive decline elderly. Since previous studies have linked reduced complexity electroencephalography (EEG) signal process decline, we propose use non-linear methods characterise changes EEG induced by NFT. In this study, analyse pre- and post-training from 11 elderly subjects who performed an NFT based on motor imagery (MI-NFT). Spectral were studied using relative...

Neurofeedback (NF) is a paradigm that allows users to self-modulate patterns of brain activity. It implemented with closed-loop brain-computer interface (BCI) system analyzes the user's activity in real-time and provides continuous feedback. This great interest due its potential as non-pharmacological non-invasive alternative treat non-degenerative disorders. Nevertheless, currently available NF frameworks have several limitations, such lack wide variety analysis metrics or overly simple...

Objective. The aim of this study was to solve one the current limitations for characterization brain network in Alzheimer's disease (AD) continuum. Nowadays, frequency-dependent approaches have reached contradictory results depending on frequency band under study, tangling possible clinical interpretations.Approach. To overcome issue, we proposed a new method build multiplex networks based canonical correlation analysis (CCA). Our determines two basis vectors using source and electrode-level...

Code-modulated visual evoked potentials (c-VEPs) are an innovative control signal utilized in brain-computer interfaces (BCIs) with promising performance. Prior studies on steady-state (SSVEPs) have indicated that the spatial frequency of checkerboard-like stimuli influences both performance and user experience. Spatial refers to dimensions individual squares comprising stimulus, quantified cycles (i.e., number black-white pairs) per degree angle. However, specific effects this parameter...

Background Public speaking is an indispensable skill that can profoundly influence success in both professional and personal spheres. Regrettably, managing anxiety during a speech poses significant challenge for many of the population. This research assessed impacts Corp-Oral program, designed to manage public university students, based on, body awareness, embodied message techniques, simulation, visualization, transformation, gesture enhancement. Methods Thirty-six students (61% women; M...