基于编码调制视觉诱发电位脑机接口滤波算法的性能比较

脑机接口(brain-computer interface,BCI)的分类性能一定程度上取决于对脑电信号的预处理方法,这项研究提出了一种空域时域滤波的预处理方法,以解决人类视觉系统中的潜伏延迟对编码调制视觉诱发电位(c-VEP) BCI的目标识别性能的影响。基于一个平均信号和单次试验信号之间的最小均方误差(the least mean square error,LMSE)创建时域空域滤波器,并且通过最小绝对收缩和选择算子(the least absolute shrinkage and selection operator,LASSO)将稀疏约束应用于滤波器的权重系数,并用模板匹配法来对目标进行识别。将算法应用于由63比特的M序列及其循环移位序列调制的16个目标的c-VEP BCI,并与通用的空域滤波算法典型相关分析(CCA)及空域时域逆滤波算法进行比较。结果表明本研究所提出的算法在分类准确率方面优于其他两种算法。

Performance comparison of filtering algorithm of BCI based on c-VEP

The classification performance of brain-computer interface (BCI) depends on the preprocessing method of EEG signals to some extent. In this study, we propose a preprocessing method for spatiotemporal filtering to solve the effect of latency delay in the human visual system on target recognition performance of code modulated visual evoked potential (c-VEP) BCI. A spatiotemporal filter was created based on the least mean square error (LMSE) between an average signal and a single test signal, and a sparsity constraint was applied to the weight coefficient of the filter by the least absolute shrinkage and selection operator (LASSO), and Template matching method was used to identify the target. The algorithm was applied to the c-VEP BCI of 16 targets modulated by the 63-bit M-sequence and its cyclic shift sequence, and compared with the conventional canonical correlation analysis (CCA) spatial filtering algorithm and spatiotemporal inverse filtering algorithm. The results of twelve subjects show that our proposed algorithm outperforms other two algorithms in classification accuracy.