An Extensive Review on Feature Extraction for Designing Brain Computer Interface

Electroencephalograph (EEG) is useful modality nowadays which is utilized to capture cognitive activities in the form of a signal representing the potential for a given period. Brain Computer Interface (BCI) systems are one of the practical application of EEG signal. Response to mental task is a well-known type of BCI systems which augments the life of disabled persons to communicate their core needs to machines that can able to distinguish among mental states corresponding to thought responses to the EEG. The success of classification of these mental tasks depends on the pertinent set formation of features (analysis, extraction and selection) of the EEG signals for the classification process. This paper presents a review study of various features transformation techniques for EEG signal, which can be very useful in designing of any type of BCI system.