基于时频特征优化选择的运动想象脑电信号分类研究
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摘要
针对运动想象脑电信号在频域和时域方面普遍存在的个体差异,提出利用滑动窗技术对运动想象过程的频率段和时间段进行分解,得到各种频率段和时间段的组合。在每种组合下,分别进行三分类运动想象脑电数据的分类实验,以分类正确率为标准,找出最佳频率段和时间段,并将其应用于运动想象脑电信号的个性化特征分析中,以进一步提高BCI系统的分类正确率。
Because individual differences generally existed in the motor imagery EEG signals in aspects of frequency and time domains, the method of using sliding window to decompose the frequency range and time range of motor image ERD/ERS phenomenon was proposed to get various combinations of frequency and time. In each combination, three classifications of motor imagery EEG experiments were made on different subjects. The best combination of frequency and time of each subject has been found out, and the personalized characteristic analysis of motor imagery EEG signals has been realized. The research has further improved BCI system's correct classification rate.
Because individual differences generally existed in the motor imagery EEG signals in aspects of frequency and time domains, the method of using sliding window to decompose the frequency range and time range of motor image ERD/ERS phenomenon was proposed to get various combinations of frequency and time. In each combination, three classifications of motor imagery EEG experiments were made on different subjects. The best combination of frequency and time of each subject has been found out, and the personalized characteristic analysis of motor imagery EEG signals has been realized. The research has further improved BCI system's correct classification rate.
引文
[1]贾花萍,赵俊龙.脑电信号分析方法与脑机接口技术[M].北京:科学出版社,2016:12-15.
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[3]马也,常天庆,郭理彬.运动想象脑电信号特征提取与分类算法研究[J].计算机工程与应用,2017,53(16):149-154.
[4] ANG K K, CHIN Z Y, ZHANG H, et al. Mutual information-based selection of optimal spatial-temporal patterns for single-trial EEG-based BCIs[J]. Pattern recognition, 2012, 45(6):2137-2144.
[5]王东洋.基于运动想象的脑电信号识别算法的研究[D].上海:华东理工大学,2013.
[6]巩笑晓.运动想象脑电信号的特征提取算法研究[D].合肥:安徽大学,2014.
[7]吴小培,周蚌艳,张磊,等.运动想象脑-机接口中的ICA滤波器设计[J].生物物理学报,2014,30(7):540-554.
[8]胡盼.基于独立分量分析的在线脑-机接口系统研究与实现[D].合肥:安徽大学,2016.
[9]陈黎黎,国红军,陈国龙.基于ERD/ERS现象的运动想象脑电信号分类研究[J].陕西科技大学学报,2015,33(4):157-161.
[2]徐欣,王娜.四类运动想象脑电信号的特征提取与分类[J].南京邮电大学学报(自然科学版),2017,37(6):18-22.
[3]马也,常天庆,郭理彬.运动想象脑电信号特征提取与分类算法研究[J].计算机工程与应用,2017,53(16):149-154.
[4] ANG K K, CHIN Z Y, ZHANG H, et al. Mutual information-based selection of optimal spatial-temporal patterns for single-trial EEG-based BCIs[J]. Pattern recognition, 2012, 45(6):2137-2144.
[5]王东洋.基于运动想象的脑电信号识别算法的研究[D].上海:华东理工大学,2013.
[6]巩笑晓.运动想象脑电信号的特征提取算法研究[D].合肥:安徽大学,2014.
[7]吴小培,周蚌艳,张磊,等.运动想象脑-机接口中的ICA滤波器设计[J].生物物理学报,2014,30(7):540-554.
[8]胡盼.基于独立分量分析的在线脑-机接口系统研究与实现[D].合肥:安徽大学,2016.
[9]陈黎黎,国红军,陈国龙.基于ERD/ERS现象的运动想象脑电信号分类研究[J].陕西科技大学学报,2015,33(4):157-161.