基于时延空时滤波的P300波形提取及目标分类算法

提出一种基于时延空时滤波的P300波形提取及目标分类算法.将多通道脑电信号进行时延，利用最小二乘法思想构造代价函数，通过交替优化的方式估计空时滤波器和源信号，使代价函数收敛并得到空时滤波器，实现空域的源分离和时域的波形提取.经过仿真P300数据对算法性能进行验证，结果表明，该算法对P300波形恢复效果优于同类型的相关算法.对真实脑电数据进行处理，用算法得到的空时滤波器提取P300源成分作为分类特征，利用训练集得到的P300源成分训练Fisher分类器进行目标分类.结果表明，算法的P300波形提取效果、目标分类准确率及AUC值均优于同类型的相关算法.因此，该算法可有效提取P300波形并进行目标分类. 

P300 Waveform Extraction and Target Classification Algorithm Based on Temporal-Delayed and Spatio-Temporal Filtering

In this study, an algorithm of P300 waveform extraction and target classification was proposed based on temporal-delayed and spatio-temporal filtering. Firstly, the multi-channel electroencephalogram (EEG) signal was delayed in temporal domain. And a cost function was constructed based on the least square method. The alternately optimizing was conducted to estimate the spatio-temporal filter and the desired signal until the cost function was converged. At last, the spatio-temporal filter could be obtained to separate the components in the spatial domain and extract the P300 waveform in the temporal domain. And then, simulation analysis was carried out to verify the waveform extraction performance of the algorithm with P300 data. The results show that the algorithm is better than the correlative algorithm for P300 waveform recovery. Finally, the obtained spatio-temporal filter was utilized to extract P300 components as classification features from real EEG data. A Fisher linear discriminant analysis was trained with the P300 components got from training dataset and utilized to classify the EEG signals. The results indicated that the P300 waveform extraction performance, classification accuracy rate and area under curve (AUC) value of the proposed algorithm are significantly better than the correlative algorithm. Therefore, the proposed algorithm can extract P300 waveform and classify target effectively.