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
|
摘要
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。
The classification performance of brain-computer interface( BCI) depends on the preprocessing method of EEG signals to some extent. A preprocessing method for spatiotemporal filtering was proposed 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 the proposed algorithm outperforms other two algorithms in classification accuracy.
The classification performance of brain-computer interface( BCI) depends on the preprocessing method of EEG signals to some extent. A preprocessing method for spatiotemporal filtering was proposed 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 the proposed algorithm outperforms other two algorithms in classification accuracy.
引文
1 Wolpaw J R,Birbaumer N,Mc Farland D J,et al.Brain-computer interfaces for communication and control[J].Clinical Neurophysiology,2002,113(6):767-791
2 Gao S,Wang Y,Gao X,et al.Visual and auditory brain-computer interfaces[J].IEEE Transactions on Biomedical Engineering,2014,61(5):1436-1447
3 Bin G,Gao X,Wang Y,et al.VEP-based brain-computer interfaces:time,frequency,and code modulations[J].IEEE Computational Intelligence Magazine,2009,4(4):22-26
4 Bin G,Gao X,Wang Y,et al.A high-speed BCI based on code modulation VEP[J].Journal of Neural Engineering,2011,8(2):025015
5 Di Russo F,Spinelli D.Electrophysiological evidence for an early attentional mechanism in visual processing in humans[J].Vision Research,1999,39(18):2975-2985
6 Di Russo F,Teder-Slejrvi W A,Hillyard S A.Steady-state VEPand attentional visual processing,the cognitive electrophysiology of mind and brain[M].Combridge:Academic Press,2003:259-274
7 Falsini B,Porciatti V.The temporal frequency response function of pattern ERG and VEP:changes in optic neuritis[J].Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section,1996,100(5):428-435
8 Wittevrongel B,Van Wolputte E,Van Hulle M M.Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding[J].Scientific Reports,2017,7(1):15037
9 Sato J,Washizawa Y.Neural decoding of code modulated visual evoked potentials by spatio-temporal inverse filtering for brain computer interfaces[C]//2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC).New York:IEEE,2016:1484-1487
10 Schmidt M.Least squares optimization with L1-norm regularization[J].CS542B Project Report,2005,504:195-221
11 Wei Q,Liu Y,Gao X,et al.A novel c-VEP BCI paradigm for increasing the number of stimulus targets based on grouping modulation with different codes[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2018,26(6):1178-1187
2 Gao S,Wang Y,Gao X,et al.Visual and auditory brain-computer interfaces[J].IEEE Transactions on Biomedical Engineering,2014,61(5):1436-1447
3 Bin G,Gao X,Wang Y,et al.VEP-based brain-computer interfaces:time,frequency,and code modulations[J].IEEE Computational Intelligence Magazine,2009,4(4):22-26
4 Bin G,Gao X,Wang Y,et al.A high-speed BCI based on code modulation VEP[J].Journal of Neural Engineering,2011,8(2):025015
5 Di Russo F,Spinelli D.Electrophysiological evidence for an early attentional mechanism in visual processing in humans[J].Vision Research,1999,39(18):2975-2985
6 Di Russo F,Teder-Slejrvi W A,Hillyard S A.Steady-state VEPand attentional visual processing,the cognitive electrophysiology of mind and brain[M].Combridge:Academic Press,2003:259-274
7 Falsini B,Porciatti V.The temporal frequency response function of pattern ERG and VEP:changes in optic neuritis[J].Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section,1996,100(5):428-435
8 Wittevrongel B,Van Wolputte E,Van Hulle M M.Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding[J].Scientific Reports,2017,7(1):15037
9 Sato J,Washizawa Y.Neural decoding of code modulated visual evoked potentials by spatio-temporal inverse filtering for brain computer interfaces[C]//2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC).New York:IEEE,2016:1484-1487
10 Schmidt M.Least squares optimization with L1-norm regularization[J].CS542B Project Report,2005,504:195-221
11 Wei Q,Liu Y,Gao X,et al.A novel c-VEP BCI paradigm for increasing the number of stimulus targets based on grouping modulation with different codes[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2018,26(6):1178-1187
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |