The significance of deep learning techniques in relation to steady‐state visually evoked
potential‐(SSVEP‐) based brain‐computer interface (BCI) applications is assessed through …

The exact nature, harmful effects and aetiology of autism spectrum disorder (ASD) have
caused widespread confusion. Artificial intelligence (AI) science helps solve challenging …

Brain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from
diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI …

Autism spectrum disorder (ASD) is a complex neurobehavioral condition that begins in
childhood and continues throughout life, affecting communication and verbal and behavioral …

To enhance the treatment of motor function impairment, patients' brain signals for self-control
as an external tool may be an extraordinarily hopeful option. For the past 10 years …

The modeling of smart training environments (STEs) for motor imagery-based brain–
computer interface (MI-BCI) falls under the multi-attribute decision analysis (MADA) due to …

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

Abstract Brain-Computer Interface (BCI) research is considered one of the significant
interdisciplinary fields. It assists people with severe motor disabilities to recover and improve …

Benchmarking brain–computer interface (BCI) applications, considering all available smart
training environment (STE) criteria, is a challenging task due to the following issues …

Background and objective: Principally, the procedure of pattern recognition in terms of
segmentation plays a significant role in a BCI-based wheelchair control system for avoiding …