An Algorithm for Idle-State Detection and Continuous Classifier Design in Motor-Imagery-Based BCI

The development of asynchronous brain-computer interface (BCI) based on motor imagery (MI) poses the research in algorithms for detecting the nontask states (I.e.,idle state) and the design of contin-uous classifiers that classify continuously incoming electroencephalogram (EEG) samples.An algorithm is proposed in this paper which integrates two two-class classifiers to detect idle state and utilizes a sliding window to achieve continuous outputs.The common spatial pattern (CSP) algorithm is used to extract features of EEG signals and the linear support vector machine (SVM) is utilized to serve as classifier.The algorithm is applied on dataset Ivb of BCI competition III,with a resulting mean square error of 0.66.The result indicates that the proposed algorithm is feasible in the first step of the development of asynchronous systems.