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Materias / palabras clave: Medical; General; Medicina

Understanding neural responses to varying physical loads is essential for developing ergonomic designs. Conventional methods for analyzing peripheral muscle activity, such as electromyography (EMG) and kinematic analysis...

Abstract Social decision-making involves intricate and dynamic interactions between brains, yet prior hyperscanning research primarily concentrated on investigating the overall patterns of interbrain synchrony (IBS), lea...

"The use of Brain-Computer Interfaces can provide substantial improvements to the quality of life of patients with diseases such as severe Amyotrophic lateral sclerosis that cause Locked-in syndrome, by creating new aven...

Materias / palabras clave: Computer science; Independent component analysis; Scrolling; Toolbox; Interface (matter); Artifact (error); Graphical user interface; Data mining; Plug-in; MATLAB; Artificial intelligence; Programming language

Materias / palabras clave: Profesores; Capacitación

Reading fluency depends on rapid integration of print and speech, yet the spatiotemporal mechanisms supporting this integration in school aged children remain poorly understood. This study combined fMRI and EEG to examin...

The electroencephalogram (EEG) is an electrophysiological monitoring method that allows us to glimpse the electrical activity of the brain. Neural oscillations patterns are perhaps the best salient feature of EEG as they...

We present a methodological framework for evaluating temporal sensitivity in frontal EEG recordings during cycling. The approach departs from randomized train–test splits and instead implements contiguous temporal with...

Brain–computer interfaces (BCIs) based on motor imagery (MI) provide a transformative direct communication pathway for patients with severe motor disabilities. However, the high inter-subject variability and non-statio...

Abstract Hybrid Brain–Computer Interface (BCI) systems that combine electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) achieve higher classification accuracy but face computational bottlenec...

Spiking neural networks (SNNs) offer a biologically inspired, energy-efficient alternative to conventional artificial neural networks (ANNs). However, deep SNNs struggle to process complex EEG signals because their spike...

Abstract The current work on emotion recognition considering EEG signals has attracted significant attention due to its advantages in fields like mental health, neural computing, and human-computer interaction. Nonethele...

Abstract Detecting epileptic seizures from electroencephalogram (EEG) signals presents a significant challenge due to the nonlinear and non-stationary nature of brain activity. In this study, we propose a hybrid classifi...

Abstract Objectives To explore the efficacy and potential neurophysiological mechanisms of 77.5 Hz transcranial alternating current stimulation (tACS) in the treatment of female adolescent non-suicidal self-injury (NSSI...

Abstract Background Chronic Insomnia Disorder (CID) is a prevalent condition. This study examines microbaric oxygen therapy (MOT) as a non-pharmacological intervention for CID patients, utilizing EEG to investigate its e...

Extensive research has demonstrated that music enhances cognitive functions, such as learning, reasoning, and memory. This study investigated the influence of music on short- and long-term memory while learning through t...

Sleep plays an important role in physical health, cognition, and emotional well-being. Since sleep disorders can affect all these aspects, accurately detecting sleep stages is key to proper diagnosis and monitoring. Auto...

Abstract Salient sounds in the environment automatically capture our attention, causing a shift of focus away from ongoing goal-directed tasks. Studies of cognitive flexibility can employ such paradigms to examine how th...

"This paper presents a statistical signal processing method for the characterization of EEG of patients suffering from epilepsy. A statistical model is proposed for the signals and the Kullback-Leibler divergence is used...

Abstract Background Psychopathic characteristics are associated with an elevated risk for violent behavior and are therefore of interest in research studies. Despite extensive research, the role of emotional and attentio...

"This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computat...

Electroencephalography (EEG) is a crucial technique for interpreting neurocognitive functions and the underlying neural information. Despite its advantages, such as high portability, cost-effectiveness and exceptional te...