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

Thanks to the fast evolution of electroencephalography (EEG)-based brain-computer
interfaces (BCIs) and computing technologies, as well as the availability of large EEG …

Non-stationarity of EEG signals leads to high variability between subjects, making it
challenging to directly use data from other subjects (source domain) for the classifier in the …

This study examines the efficacy of various neural network (NN) models in interpreting
mental constructs via electroencephalogram (EEG) signals. Through the assessment of 16 …

Motor Imagery (MI) classification with electroencephalography (EEG) is a critical aspect of
Brain–Computer Interface (BCI) systems, enabling individuals with mobility limitations to …

Abstract Domain adaptation (DA) plays a crucial role in achieving subject-independent
performance in Brain-Computer Interface (BCI). However, previous studies have primarily …

Deep learning has revolutionized EEG decoding, showcasing its ability to outperform
traditional machine learning models. However, unlike other fields, EEG decoding lacks …

Brain-computer interface (BCI) is a technology that directly connects signals between the
human brain and a computer or other external device. Motor imagery …

Abstract Brain–computer interfaces (BCIs), representing a transformative form of human–
computer interaction, empower users to interact directly with external environments through …

Objective. The combination of the motor imagery (MI) electroencephalography (EEG) signals
and deep learning-based methods is an effective way to improve MI classification accuracy …