Brain–Computer Interfacing Using EEG

Development of brain–computer interfaces (BCIs) during the last three decades has enabled communications or control over external devices such as computers and artificial prostheses with the electrical activity of the human nervous system. The BCI applications are being more extended to human use especially for rehabilitation purposes. The correspondence between electroencephalogram (EEG) patterns and computer actions constitutes a machinelearning problem since the computer should learn how to recognize a given EEG pattern. Feature extraction and classification of the features for each particular body movement is the main objective in most of the BCI systems. Neuroprosthesis, as the major application of neurotechnology, together with BCI can help restore function for people with neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. Estimation of the cortical connectivity patterns provides a new tool in evaluation of the directivity of brain signals and localization of the movementrelated sources.