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 …

Emotion, a fundamental trait of human beings, plays a pivotal role in shaping aspects of our
lives, including our cognitive and perceptual abilities. Hence, emotion recognition also is …

Subtle changes in fine motor control and quantitative electroencephalography (qEEG) in
patients with mild cognitive impairment (MCI) are important in screening for early dementia …

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

Early diagnosis of Alzheimer's disease (AD) is a very challenging problem and has been
attempted through data-driven methods in recent years. However, considering the inherent …

This study compared the efficacy of Motor Imagery brain-computer interface (MI-BCI)
combined with physiotherapy and physiotherapy alone in ischemic stroke before and after …

The emergence of deep learning methods has driven the widespread use of brain–machine
interface motor imagery classification in machine control and medical rehabilitation, and has …

Electroencephalogram signals (EEG) have provided biometric identification systems with
great capabilities. Several studies have shown that EEG introduces unique and universal …

This paper proposes a real-time emotion recognition system that utilizes
photoplethysmography (PPG) and electromyography (EMG) physiological signals. The …

(1) Objective: To investigate the feasibility, safety, and effectiveness of a brain–computer
interface (BCI) system with visual and motor feedback in limb and brain function …