Investigations on Motor Imagery in Brain–Computer Interface

Abstract

AbstractThe authors present an investigation on state of the art of motor imagery (MI) for brain-computer interface (BCI) using electroencephalogram (EEG) in this paper. The EEG-based BCI is the youngest and fastest growing emergent technology that permits encephalic activity alone to computer or artificial system. Motor imagery BCI systems have enticed the great interest ranging from medicine to military points. EEG has been used for medical diagnosis such as seizure detection, brain injury detection, and also used control engineering for object controlling application, object recognition, rehabilitation, and human assistance. The EEG-based BCI system, wavelet transform gives appropriates outputs which enhanced the classification results. The support vector machine (SVM) and deep learning techniques generally used for EEG-based MI-BCI classification tasks. This research shows clear and easy interpretation of each methods used for feature extraction and classification using EEG for motor imagery BCI systems.KeywordsArtifactsBrain computer interfaceElectroencephalogramMotor imagery