Electroencephalograms (EEG)-based technology for recognizing emotions has attracted a
lot of interest lately. However, there is still work to be done on the efficient fusion of different …

Motor imagery (MI)-based electroencephalography (EEG) stands as a prominent paradigm
in the brain–computer interface (BCI) field, which is frequently applied in neural …

Objective. Machine learning has enhanced the performance of decoding signals indicating
human behaviour. Electroencephalography (EEG) brainwave decoding, as an exemplar …

Motor Imagery (MI) Electroencephalography (EEG) is one of the most common Brain-
Computer Interface (BCI) paradigms that has been widely used in neural rehabilitation and …

Electroencephalogram (EEG) signals are widely applied in emotion recognition, sentiment
analysis, disease classification, sleep disorder identification, and fatigue detection. Recent …

The brain is a complex system comprising neurons, local circuits, and functional regions.
Neural pathways link various regions and collaborate to accomplish intricate cognitive and …

Human motor learning is a neural process essential for acquiring new motor skills and
adapting existing ones, which is fundamental to everyday activities. Neurological disorders …

One of the most promising applications for electroencephalogram (EEG)-based brain–
computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks …

Same limb motor imagery (MI) brain-computer interfaces can effectively overcome the
cognitive disassociation problem of the traditional different-limb MI paradigm, and they can …

Electroencephalography (EEG) is a vital tool to measure and record brain activity in
neuroscience and clinical applications, yet its potential is constrained by signal …