Brain-computer interface (BCI) based on electroencephalogram (EEG) signals can provide a
way for human to communicate with the outside world. This approach is independent of the …

This paper proposes a novel feature selection method utilizing Rényi min-entropy-based
algorithm for achieving a highly efficient brain–computer interface (BCI). Usually, wavelet …

In recent years, brain–machine interface (BMI) technology has made great progress in
controlling external devices and restoring motor function for people with disabilities. To …

The extraction methodology of the significant features from the signals is one of the most
important pre-requisite steps for EEG signal classification. Common spatial pattern (CSP) is …

Common spatial pattern (CSP) is a popular feature extraction method for
electroencephalogram (EEG) motor imagery (MI). This study modifies the conventional CSP …

As a multichannel spatial filtering technique, common spatial patterns (CSP) have been
successfully applied in brain‐computer interfaces (BCI) community based on …

This study addresses the challenge faced by individuals with upper-limb prostheses in
regulating grip force and adapting movements to different object weights. Despite limited …

Upper extremity prostheses vary based on patients' articulation levels and movement
methods. They can be cosmetic, operate mechanically with shoulder movement, or be …

Stress is a response to various environmental, psychological, and social factors, resulting in
strain and pressure on individuals. Categorizing stress levels is a common practise, often …

In brain computer interface (BCI) systems, the electroencephalography (EEG) signals give a
pathway to a motor disabled person to communicate outside using the brain signal and a …