Electroencephalogram (EEG) plays an important role in identifying brain activity and
behavior. However, the recorded electrical activity always be contaminated with artifacts and …

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 …

Brain–computer interfaces (BCIs) development is closely related to physics. In this paper, we
review the physical principles of BCIs, and underlying novel approaches for registration …

Objective: This study proposes and evaluates a novel data-driven spatial filtering approach
for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high …

Brain-computer interfaces (BCIs) using signals acquired with intracortical implants have
achieved successful high-dimensional robotic device control useful for completing daily …

ERPLAB toolbox is a freely available, open-source toolbox for processing and analyzing
event-related potential (ERP) data in the MATLAB environment. ERPLAB is closely …

This paper presents an extensive review on the artifact removal algorithms used to remove
the main sources of interference encountered in the electroencephalogram (EEG) …

Background Electroencephalographic data are easily contaminated by signals of non-neural
origin. Independent component analysis (ICA) can help correct EEG data for such artifacts …

Objective. Deep learning (DL) networks are increasingly attracting attention across various
fields, including electroencephalography (EEG) signal processing. These models provide …

Using electroencephalography (EEG) to elucidate the spontaneous activation of brain
resting-state networks (RSNs) is nontrivial as the signal of interest is of low amplitude and it …