Decoding of motor imagery (MI) from Electroencephalogram (EEG) is an important
component of BCI system that helps motor-disabled people interact with the outside world …

In building a practical and robust brain-computer interface (BCI), the classification of motor
imagery (MI) from electroencephalography (EEG) across multiple days is a long-standing …

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 …

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 (MI) is a typical BCI paradigm and has been widely applied into many
aspects (eg brain-driven wheelchair and motor function rehabilitation training). Although …

The brain computer interface (BCI) aimed to offer an improved and quality life for people
having disabilities. Various physiological sensors are utilized for designing the BCI …

Brain computer interfaces (BCIs) have been widely adopted to enhance human perception
via brain signals with abundant spatial-temporal dynamics, such as electroencephalogram …

Motor imagery (MI) based brain-computer interface systems (BCIs) are highly in need for a
large number of real-time applications such as hands and touch-free text entry system …

Due to their vital applications in many real-world situations, researchers are still presenting
bunches of methods for better analysis of motor imagery (MI) electroencephalograph (EEG) …

The detection of Schizophrenia (SZ) directly from brain Electroencephalogram (EEG) signals
has recently gained importance. Traditionally, EEG-based SZ detection is done by either …