Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …

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 Data augmentation (DA) has recently been demonstrated to achieve
considerable performance gains for deep learning (DL)—increased accuracy and stability …

Abstract In 1999, the International Federation of Clinical Neurophysiology (IFCN) published
“IFCN Guidelines for topographic and frequency analysis of EEGs and EPs”(Nuwer et al …

Magnetoencephalography and electroencephalography (M/EEG) measure the weak
electromagnetic signals generated by neuronal activity in the brain. Using these signals to …

Magnetoencephalography and electroencephalography (M/EEG) measure the weak
electromagnetic signals originating from neural currents in the brain. Using these signals to …

The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a
trend towards more powerful multivariate analysis methods. Often it is desired to interpret the …

In this paper, we propose a hierarchical bidirectional Gated Recurrent Unit (GRU) network
with attention for human emotion classification from continues electroencephalogram (EEG) …

As the EEG inverse problem does not have a unique solution, the sources reconstructed
from EEG and their connectivity properties depend on forward and inverse modeling …

Following the research question and the relevant dataset, feature extraction is the most
important component of machine learning and data science pipelines. The wavelet …