Applications of machine learning (ML) methods have been used extensively to solve various
complex challenges in recent years in various application areas, such as medical, financial …

Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …

Background and objective: We have cast the net into the ocean of knowledge to retrieve the
latest scientific research on deep learning methods for physiological signals. We found 53 …

The electroencephalography (EEG) signal is a noninvasive and complex signal that has
numerous applications in biomedical fields, including sleep and the brain–computer …

Objective. Processing strategies are analyzed with respect to the classification of
electroencephalographic signals related to brain-computer interfaces (BCIs) based on motor …

This paper presents a benchmark steady-state visual evoked potential (SSVEP) dataset
acquired with a 40-target brain-computer interface (BCI) speller. The dataset consists of 64 …

The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide
high quality neuroscientific data for open access to the scientific community. As experienced …

Machine learning and pattern recognition algorithms have in the past years developed to
become a working horse in brain imaging and the computational neurosciences, as they are …

A brain-computer interface (BCI) is a system that allows its users to control external devices
with brain activity. Although the proof-of-concept was given decades ago, the reliable …

We provide an open access dataset for hybrid brain-computer interfaces (BCIs) using
electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we …