A novel method for motor imagery EEG adaptive classification based biomimetic pattern recognition
The real on-line BCI is indeed a hotspot at present whose performance however is limited by the problems of non-stationary etc. In this paper, a novel method for the adaptive classification of motor imagery EEG data based Biomimetic Pattern Recognition (BPR) through introducing three adaptive operators is proposed. Considering that the large amounts of labeled samples are difficult to get in the actual application, we also propose a novel unsupervised scheme based the adaptive classifier to solve this problem. Sufficient experiments are conducted on the datasets from previous Brain–Computer Interface Competitions and the actual on-line EEG data in adaptive scheme. The results demonstrate that the new algorithm is efficiency and robust compared with non-adaptive classifiers. Besides, a couple of analyses are made on the selection of parameters in the adaptive BPR, and some advice has been come up with about their selections.