摘要：For a robust brain-computer interface (BCI) system based on motor imagery (MI), it
should be able to tell when the subject is not concentrating on MI tasks
(the “idle state”) so that real MI tasks could be extracted accurately. Moreover,
because of the diversity of idle state, detecting idle state without training samples is as
important as classifying MI tasks. In this paper, we propose an algorithm for solving this
problem. A three-class classifier was constructed by combining two two-class classifiers, one
specified for idle-state detection and the other for these two MI tasks. Common spatial
subspace decomposition (CSSD) was used to extract the features of event-related
desynchronization (ERD) in two motor imagery tasks. Then Fisher discriminant analysis
(FDA) was employed in the design of two two-class classifiers for completion of detecting
each task, respectively. The algorithm successfully provided a way to solve the problem
of “idle-state detection without training samples.” The algorithm was applied
to the dataset IVc from BCI competition III. A final result with mean square error of 0.30 was
obtained on the testing set. This is the winning algorithm in BCI competition III. In addition,
the algorithm was also validated by applying to the EEG data of an MI experiment including
“idle” task.
