生物医学工程学杂志

生物医学工程学杂志

基于 Fisher 准则的单次运动想象脑电信号意图识别研究

查看全文

为了实现脑机接口系统,对单次运动想象脑电信号的优化特征进行了提取与分类。针对运动想象脑电信号的特点,通过预处理得到脑电信号 Mu 节律成分,利用共同空间模式算法在优化空间滤波下提取了运动想象脑电信号的特征。使用 Fisher 判别分析进行分类决策,并通过交叉验证与受试者操作曲线相结合的方法对分类性能进行综合评价。以交叉验证方式对应用空间滤波进行投影的特征维度确定问题进行深入讨论,评价结果表明本文方法在保证较高的准确率的同时可提高运行速度。基于优化的脑电特征进行运动想象意图分类,能够反映不同状态的差异并简化识别流程,为意图识别研究提供了一种准确高效的新方法。

In order to realize brain-computer interface (BCI), optimal features of single trail motor imagery electroencephalogram (EEG) were extracted and classified. Mu rhythm of EEG was obtained by preprocessing, and the features were optimized by spatial filtering, which are estimated from a set of data by method of common spatial pattern. Classification decision can be made by Fisher criterion, and classification performance can be evaluated by cross validation and receiver operating characteristic (ROC) curve. Optimal feature dimension determination projected by spatial filter was discussed deeply in cross-validation way. The experimental results show that the high discriminate accuracy can be guaranteed, meanwhile the program running speed is improved. Motor imagery intention classification based on optimized EEG feature provides difference of states and simplifies the recognition processing, which offers a new method for the research of intention recognition.

关键词: 脑电; 运动想象; 特征优化; Fisher判别; 受试者操作曲线

Key words: electroencephalogram; motor imagery; features optimization; fisher discriminant analysis; receiver operating characteristic curve

登录后 ,请手动点击刷新查看全文内容。 没有账号,
登录后 ,请手动点击刷新查看图表内容。 没有账号,
1. Cengiz B, Boran H E. The role of the cerebellum in motor imagery. Neurosci Lett, 2016, 617: 156-159.
2. Fu Yunfa, Xu Baolei, Li Yongcheng, et al. Single-trial decoding of imagined grip force parameters involving the right or left hand based on movement-related cortical potentials. Chinese Science Bulletin, 2014, 59(16): 1907-1916.
3. 李松, 伏云发, 杨秋红, 等. 基于左右手运动想象单通道脑电信号的预处理研究. 生物医学工程学杂志, 2016, 33(5): 862-866.
4. Lemm S, Blankertz B, Curio G, et al. Spatio-spectral filters for improving the classification of single trial EEG. IEEE Trans Biomed Eng, 2005, 52(9): 1541-1548.
5. Andrade J, Cecílio J, Simões M, et al. Separability of motor imagery of the self from interpretation of motor intentions of others at the single trial level: an EEG study. J Neuroeng Rehabil, 2017, 14(1): 63.
6. Blankertz B, Tomioka R, Lemm S, et al. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag, 2008, 25(1): 41-56.
7. Blankertz B, Dornhege G, Krauledat M, et al. The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects. Neuroimage, 2007, 37(2): 539-550.
8. Barachant A, Bonnet S, Congedo M, et al. Common Spatial Pattern revisited by Riemannian geometry//IEEE International Workshop on Multimedia Signal Processing. Saint Malo, France: IEEE, 2010: 472-476.
9. Li Xiaomeng, Lu Xuesong, Wang Haixian. Robust common spatial patterns with sparsity. Biomed Signal Process Control, 2016, 26: 52-57.
10. Ramoser H, Müller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering, 2000, 8(4): 441-446.
11. 舒醒, 于慧敏, 郑伟伟, 等. 基于边际Fisher准则和迁移学习的小样本集分类器设计算法. 自动化学报, 2016, 42(9): 1313-1321.
12. 李静, 王金甲, 洪文学. 机器学习多类接收机工作特性研究. 生物医学工程学杂志, 2012, 29(1): 170-174.
13. Duda R O, Hart P E, Stork D G. Pattern classification. 2nd ed. New York: Wiley Interscience, 2001: 34-35.