Single trial classification of motor imagery electroencephalogram based on Fisher criterion_Journal of Biomedical Engineering

Authors：

 FU Rongrong , HOU Peiguo , LI Mandi

School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China;

Corresponding author：

FU Rongrong, Email: frr1102@aliyun.com

Keywords：

electroencephalogram; motor imagery; features optimization; Fisher discriminant analysis; receiver operating characteristic curve

DOI：

10.7507/1001-5515.201701040

Video：

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Abstract Full text Figures/Tables Video References Cited by

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.

Citation： FU Rongrong, HOU Peiguo, LI Mandi. Single trial classification of motor imagery electroencephalogram based on Fisher criterion. Journal of Biomedical Engineering, 2018, 35(5): 774-778. doi: 10.7507/1001-5515.201701040 Copy

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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.
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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.
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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.

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