Abstract
Recently, a lot of researches on Brain Computer Interfaces (BCIs) have been reported. It is expected that BCIs will help patients like those with Amyotrophic Lateral Sclerosis (ALS) to control a wheel chair or to communicate with other people just by thoughts. BCIs recognize thoughts through analysis of biological signals such as Electroencephalogram (EEG), and those associated with motor imagery have been investigated. Bereitschschafts potential (BP) and event-related desynchronization (ERD) are well known as features of EEG related to movement, and EEGs of different movements have different temporal patterns. The authors have proposed a method to extract such features and have shown that the proposed method is effective for EEGs of actual movements. In this paper the proposed method was applied to EEGs of motor imagery, and a classification accuracy for test data set was 93%.
