Predicable, concurrent and real-time transmiision of high-speed data streams in online BCI.

About online BCI based on multi-class motor imagery, how to handle high-speed EEG data streams is a diificulty for the realizing of online awareness recognition, and the key is high-speed computing and prediction under complicated conditions. This paper took thread concurrency as the entry point of high-speed computing firstly, it decomposed the task of EEG signal analysis into more thread subtasks, and solved the coordination problem brought by thread concurrency with buffer management policies; then, for the complicated change of high-speed EEG data streams s it adopted adaptive one-sided fuzzy inference to predict the telescopic change of data streams ; lastly, against the disorders of intermediate result due to thread concurrency, it designed a method of mutual exclusion and synchronization with semaphore to recombine the intermediate data blocks orderly. Numerous experiments with multiple subjects show that the average delay time of a single Trial decreases obviously. Therefore, thread concurrency and fuzzy inference can solve the problem of high-speed computing and prediction in online BCI, and improve the information transmiision rates. [ABSTRACT FROM AUTHOR]