Cheng Ming, Jia Wenyan, Gao Xiaorong, Gao Shangkai, Yang Fusheng
Department of Biomedical Engineering, Tsinghua University, Beijing 100084 China.
Clin Neurophysiol. 2004 Apr;115(4):745-51. doi: 10.1016/j.clinph.2003.11.038.
To classify the EEG data recorded in mu rhythm-based cursor control experiments with 4 possible choices.
The algorithm included preprocessing, feature extraction, and classification. Two spatial filters, common average reference and common spatial subspace decomposition, were used in preprocessing to improve the signal-to-noise ratio, and then two features were extracted based on the power spectrum and the time course of the mu rhythm respectively. A Fisher ratio was defined to select channels in feature extraction. A 2-dimensional linear classifier was trained for final classification.
Two types of classifiers were trained for the training dataset. The uniform classifier gave a classification accuracy of 76.4%, and the classifier trained by leave-one-out method gave a classification accuracy of 74.4%, both higher than the online accuracy 69.5%. The uniform classifier was applied to the test dataset and the classification accuracy was 65.9%, lower than the online accuracy 73.2%.
Spatial filtering can give a notable improvement in classification accuracy. The time course of the mu rhythm, as well as the power of the mu rhythm, shows difference between the 4 targets, and can contribute to the classification.
The spatial filtering, feature extraction and channel selection methods in the algorithm will provide some practical suggestions for further study on the mu rhythm-based brain-computer interface.
对基于μ节律的光标控制实验中记录的脑电图（EEG）数据进行分类，有4种可能的选择。
该算法包括预处理、特征提取和分类。预处理中使用了两种空间滤波器，即公共平均参考和公共空间子空间分解，以提高信噪比，然后分别基于μ节律的功率谱和时间历程提取两种特征。在特征提取中定义了一个Fisher比率来选择通道。训练一个二维线性分类器进行最终分类。
针对训练数据集训练了两种类型的分类器。均匀分类器的分类准确率为76.4%，留一法训练的分类器的分类准确率为74.4%，均高于在线准确率69.5%。将均匀分类器应用于测试数据集，分类准确率为65.9%，低于在线准确率73.2%。
空间滤波可显著提高分类准确率。μ节律的时间历程以及μ节律的功率在4个目标之间显示出差异，并有助于分类。
该算法中的空间滤波、特征提取和通道选择方法将为基于μ节律的脑机接口的进一步研究提供一些实用建议。
