关键词: 脑机接口, 自训练, 分段重叠共空间模式, 置信度评估准则

Abstract: In order to reduce the boring and time-consuming training process and improve the classification accuracy of brain-computer interface system, this paper proposes a self-training algorithm with segmented overlapping common spatial pattern algorithm which applies a semi-supervised learning to motor imagination classification. The new algorithm uses segmented overlapping common spatial pattern as feature extraction method and learns from a small pool of labeled data. In addition, a confidence criterion is proposed to select the most informative samples from the unlabeled samples to improve the classifier. With the help of small labeled data and large unlabeled data, the proposed algorithm achieves better classification performance than self-training algorithm with common spatial pattern and self-training algorithm with filter bank common spatial pattern. The dataset Iva of BCI competition III has been used to demonstrate the validity of the method, and the results show the proposed algorithm can effectively improve the classification accuracy of motor imagination.

Key words: Brain Computer Interface（BCI）, self-training, Segmented overlapping Common Spatial Pattern（SCSP）, confidence criterion