The classification recognition rate of motor imagery is a key factor to improve the
performance of brain–computer interface (BCI). Thus, we propose a feature extraction …

Analyzing electroencephalography (EEG) signals with machine learning approaches has
become an attractive research domain for linking the brain to the outside world to establish …

Liver diseases represent a significant healthcare challenge, impacting millions globally and
posing complexities in diagnosis. To address this global health concern, this paper …

Thanks to the fast evolution of electroencephalography (EEG)-based brain-computer
interfaces (BCIs) and computing technologies, as well as the availability of large EEG …

Brain-computer interface (BCI) aims to translate human intention into a control output signal.
In motor-imaginary (MI) BCI, the imagination of movement modifies the cortex brain activity …

Brain-computer interface (BCI) is a viable alternative communication strategy for patients of
neurological disorders as it facilitates the translation of human intent into device commands …

The study of EEG signals is of great significance for the diagnosis and prevention of brain
disease. Most of the previous studies are based on the binary classification of nonictal and …

In this paper, we present a genetic algorithm (GA) based band power feature sparse
learning (SL) approach for classification of electroencephalogram (EEG)(GABSLEEG) in …

Motor imagery-based electroencephalogram brain-computer interface (BCI) performance
suffers from huge variations within and across subjects. This is due to different spatial and …

It is still a big challenge to extract effective features from raw electroencephalogram (EEG)
signals and then to improve classification accuracy of motor imagery (MI) applications on …