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| Common Spatial Spectral Pattern for Motor Imagery Tasks in Small Channel Configuration |
| School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China |
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Abstract Targeting to the deficient recording condition with 2～3 channels, a novel method, multiple timedelayed common spatial spectral pattern (CSSP), was proposed in this paper. In this approach, 2～10 timedelayed Electroencephalogram (EEG) signals for each channel were used to expand the number of EEG channels and then common spatial pattern (CSP) was used to extract the features. Mutual information criterion was applied to choose the optimal number of timedelayed signals. The proposed method was tested by the IIb datasets of 9 subjects in BCI Competition 2008 and datasets of 13 subjects in our experiments. The average Kappa coefficients for the two datasets are 0.6 and 0.34, respectively. This method automatically weights the importance of frequency in 8～30 Hz and enhances the classification accuracy in the case of deficient recording consequently.
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［1］Krusienski D, GrosseWentrup M, Galan F, et al. Critical issues in stateofart braincomputer interface signal processing ［J］. Journal of Neural Engineering, 2011, 8(2): 025002.
［2］高上凯. 浅谈脑-机接口的发展现状与挑战 ［J］. 中国生物医学工程学报, 2007, 26 (6)：801-803.
［3］尧德中，刘铁军，雷旭，等. 基于脑电的脑-机接口：关键技术和应用前景 ［J］. 电子科技大学学报, 2009, 38(5)：550-554.
［4］Pfurtscheller G, Neuper C. Eventrelated synchronization of mu rhythm in the EEG over the cortical hand area in man ［J］. Neuroscience Letters, 1994, 174(1):93–96.
［5］Ramoser H, MullerGerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement ［J］. IEEE Transactions on Rehabilitation Engineering, 2000, 8(4):441–446.
［6］Lemm S, Blankertz B, Curio G, et al. Spatiospectral filters for improving the classification of single trial EEG ［J］. IEEE Transactions on Biomedical Engineering, 2005, 52(9):1541-1548.
［7］Wang Haixian, Tang Qin, Zheng Wenming. L1-normbased common spatial patterns ［J］. IEEE Transactions on Biomedical Engineering, 2012, 〖STHZ〗59(3): 653-662.
［8］Lotte F, Guan C. Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms ［J］, IEEE Transactions on Biomedical Engineering, 2011, 58(2): 355-362.
［9］Broomhead D, King G. Extracting qualitative dynamics from experimental data ［J］. Physica D, 1986, 20(2):217-236.
［10］Fukunaga K. Introduction to Statistical Pattern Recognition ［M］. Academic Press, 1990:31-35.
［11］Blankertz B, Tomioka R, Lemm S, et al. Optimizing spatial filters for Robust EEG singletrial analysis ［J］. IEEE Signal Processing Magazine, 2008, 25(1):41–56.
［12］Dornhege G, Blankertz B, Krauledat B, et al. Combined optimization of spatial and temproral filters for improving braincomputer interfacing ［J］. IEEE Transactions on Biomedical Engineering, 2006, 53(11):2274-2281.
［13］Ang K, Chin Z, Zhang H, et al. Mutual informationbased selection of optimal spatialtemporal patterns for singletrial EEGbased BCIs ［J］. Pattern Recognition, 2012, 45(6) : 2137- 2144.
［14］Zhang H, Chin Z, Ang K, et al. Optimum spatiospectral filtering network for braincomputer interface ［J］. IEEE Transactions on Neural Networks, 2011, 22(1): 52-63.［15］Duda R, Hart P, Stork D, Pattern classification ［M］// Maximumlikelihood and Bayesian Parameter Estimation. New York: John Wiley & Sons, 2001:117-124.
［16］BCI Competition IV. ［EB/OL］ http:// http://www.bbci.de/competition/iv/desc_2b.pdf, 2008-04-16/2013-01-27.
［17］Meng Jianjun, Huang Gan, Zhang Dingguo, et al. Optimizing spatial spectral patterns jointly with channel configuration for braincomputer interface ［J］. Neurocomputing, 2013, 104: 115-126.
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