中国科学院自动化研究所机构知识库

Motor imagery-based brain-computer interface (MI-BCI) has shown promising potential for improving motor function in neurorehabilitation and motor assistance among patients. However, the decoding accuracy of MI-BCI is limited by the nonstationarity and high intersubject variability of electroencephalogram (EEG) signals. Moreover, decoding MI intention based on fixed-length EEG signals will not only increase the risk of misclassification but also diminish the information transfer rate (ITR) of the BCI system. To overcome these limitations, an adaptive decoding method based on the synchronous adaptation of stimulus paradigm and classification model is proposed to realize a fast and robust MI-BCI. First, an attention-driven dynamic stopping (DS) strategy, which is designed based on the theta-to-beta ratio of EEG signals, is proposed to control the MI-related EEG acquisition time. It can adaptively minimize the data length used for classification under the ensurance of getting a credible classification result, thus improving brain-computer interaction efficiency. Then, the minimum distance to the Riemannian mean algorithm is introduced for the four-class EEG classification. To improve the classification accuracy, the classification model is adapted online based on the error-related potential (Errp) to process the nonstationary characteristics of EEG signals. The feasibility of the proposed online collaborative optimization method in fast and accurate interaction was validated on ten healthy subjects. The results show that the proposed method can significantly improve the EEG classification accuracy by 2.73% with 9.04 ITR improvement compared with that without adaptation (paired t-test, p < p 0.05). Moreover, the MI duration of 2.57 s is recommended for stimulus paradigm design to achieve a better tradeoff between accuracy and efficiency of brain-computer interaction. These phenomena further demonstrate the feasibility of the proposed method in advancing the development of MI-BCI with high efficiency, robustness, and flexibility.