Mental-tasks based brain–computer interfaces (MT-BCIs) allow their users to interact with an
external device solely by using brain signals produced through mental tasks. While MT-BCIs …

Brain-computer interface (BCI) is a rapidly evolving technology that has the potential to
widely influence research, clinical and recreational use. Non-invasive BCI approaches are …

Electroencephalography (EEG) signals collected from human brains have generally been
used to diagnose diseases. Moreover, EEG signals can be used in several areas such as …

Many deep-learning-based seizure detection algorithms have achieved good classification,
which usually outperformed traditional machine-learning-based algorithms. However, the …

Abstract Background and Objective Brain-computer interfaces (BCIs) enable people to
control an external device by analyzing the brain's neural activity. Functional near-infrared …

Background: The study examines the effectiveness of both neurofeedback and motor-
imagery brain-computer interface (BCI) training, which promotes self-regulation of brain …

Recent years have witnessed a growing interest in EEG-based wearable classifiers of
emotions, which could enable the real-time monitoring of patients suffering from …

Emotion recognition from electroencephalogram (EEG) signals requires accurate and
efficient signal processing and feature extraction. Deep learning technology has enabled the …

Abstract Current Motor Imagery Brain-Computer Interfaces (MI-BCI) require a lengthy and
monotonous training procedure to train both the system and the user. Considering many …

This paper presents a wearable brain–computer interface relying on neurofeedback in
extended reality for the enhancement of motor imagery training. Visual and vibrotactile …