Objective. The use of deep learning for electroencephalography (EEG) classification tasks
has been rapidly growing in the last years, yet its application has been limited by the …

Objective. This review paper provides an integrated perspective of Explainable Artificial
Intelligence (XAI) techniques applied to Brain-Computer Interfaces (BCIs). BCIs use …

Population-level modeling can define quantitative measures of individual aging by applying
machine learning to large volumes of brain images. These measures of brain age, obtained …

Sleep staging has a very important role in diagnosing patients with sleep disorders. In
general, this task is very time-consuming for physicians to perform. Deep learning shows …

The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has
substantially advanced human-computer interaction (HCI) technologies in the AI era …

Pathology diagnosis based on EEG signals and decoding brain activity holds immense
importance in understanding neurological disorders. With the advancement of artificial …

Background Electroencephalography (EEG) has a long history as a clinical tool to study
brain function, and its potential to derive biomarkers for various applications is far from …

Sleep abnormalities can have severe health consequences. Automated sleep staging, ie
labelling the sequence of sleep stages from the patient's physiological recordings, could …

Electroencephalography (EEG) data entail a complex spatiotemporal structure that reflects
ongoing organization of brain activity. Characterization of the spatial patterns is an …

Driving under drowsy conditions significantly escalates the risk of vehicular accidents.
Recent endeavors to prevent driving accidents have focused on using …