Xu Neng, Gao Xiaorong, Hong Bo, Miao Xiaobo, Gao Shangkai, Yang Fusheng
Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
IEEE Trans Biomed Eng. 2004 Jun;51(6):1067-72. doi: 10.1109/TBME.2004.826699.
An algorithm based on independent component analysis (ICA) is introduced for P300 detection. After ICA decomposition, P300-related independent components are selected according to the a priori knowledge of P300 spatio-temporal pattern, and clear P300 peak is reconstructed by back projection of ICA. Applied to the dataset IIb of BCI Competition 2003, the algorithm achieved an accuracy of 100% in P300 detection within five repetitions.
本文介绍了一种基于独立成分分析（ICA）的P300检测算法。在ICA分解之后，根据P300时空模式的先验知识选择与P300相关的独立成分，并通过ICA的反向投影重建清晰的P300峰值。将该算法应用于2003年脑机接口竞赛的数据集IIb，在五次重复实验中，该算法在P300检测中的准确率达到了100%。
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