L1 Norm based common spatial patterns decomposition for scalp EEG BCI

BioMedical Engineering OnLine

Peiyang Li¹  Peng Xu¹  Rui Zhang¹  Lanjin Guo¹  Dezhong Yao¹ 

[1] School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China

关键词: Singular value decomposition;    Motor imagery;    L1 norm;    Common spatial pattern;    Brain computer interface;   

Others  :  797420
DOI  :  10.1186/1475-925X-12-77

 received in 2013-05-07, accepted in 2013-08-02,  发布年份 2013

【 摘 要 】

Background

Brain computer interfaces (BCI) is one of the most popular branches in biomedical engineering. It aims at constructing a communication between the disabled persons and the auxiliary equipments in order to improve the patients’ life. In motor imagery (MI) based BCI, one of the popular feature extraction strategies is Common Spatial Patterns (CSP). In practical BCI situation, scalp EEG inevitably has the outlier and artifacts introduced by ocular, head motion or the loose contact of electrodes in scalp EEG recordings. Because outlier and artifacts are usually observed with large amplitude, when CSP is solved in view of L2 norm, the effect of outlier and artifacts will be exaggerated due to the imposing of square to outliers, which will finally influence the MI based BCI performance. While, L1 norm will lower the outlier effects as proved in other application fields like EEG inverse problem, face recognition, etc.

Methods

In this paper, we present a new CSP implementation using the L1 norm technique, instead of the L2 norm, to solve the eigen problem for spatial filter estimation with aim to improve the robustness of CSP to outliers. To evaluate the performance of our method, we applied our method as well as the standard CSP and the regularized CSP with Tikhonov regularization (TR-CSP), on both the peer BCI dataset with simulated outliers and the dataset from the MI BCI system developed in our group. The McNemar test is used to investigate whether the difference among the three CSPs is of statistical significance.

Results

The results of both the simulation and real BCI datasets consistently reveal that the proposed method has much higher classification accuracies than the conventional CSP and the TR-CSP.

Conclusions

By combining L1 norm based Eigen decomposition into Common Spatial Patterns, the proposed approach can effectively improve the robustness of BCI system to EEG outliers and thus be potential for the actual MI BCI application, where outliers are inevitably introduced into EEG recordings.

【 授权许可】

   
2013 Li et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]McFarland DJ, Wolpaw JR: Brain-computer interfaces for communication and control. Communications of the Acm 2011, 54(5):60-66.
• [2]Vaughan TM: Guest editorial brain-computer interface technology: a review of the second international meeting. Neural Systems and Rehabilitation Engineering, IEEE Transactions 2003, 11(2):94-109.
• [3]Blankertz B, Dornhege G, Krauledat M, Muller KR, Curio G: The non-invasive Berlin brain-computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage 2007, 37(2):539-550.
• [4]Laubach M, Wessberg J, Nicolelis MA: Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task. Nature 2000, 405(6786):567-571.
• [5]Blankertz B, Sannelli C, Haider S, Hammer EM, Kubler A, Muller KR, Curio G, Dickhaus T: Neurophysiological predictor of SMR-based BCI performance. Neuroimage 2010, 51(4):1303-1309.
• [6]Kubler A, Nijboer F, Mellinger J, Vaughan TM, Pawelzik H, Schalk G, McFarland DJ, Birbaumer N, Wolpaw JR: Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 2005, 64(10):1775-1777.
• [7]Daly JJ, Wolpaw JR: Brain-computer interfaces in neurological rehabilitation. Lancet Neurol 2008, 7(11):1032-1043.
• [8]Leins U, Goth G, Hinterberger T, Klinger C, Rumpf N, Strehl U: Neurofeedback for children with ADHD: a comparison of SCP and theta/beta protocols. Appl Psychophysiol Biofeedback 2007, 32(2):73-88.
• [9]Mormann F, Kreuz T, Rieke C, Andrzejak RG, Kraskov A, David P, Elger CE, Lehnertz K: On the predictability of epileptic seizures. Clin Neurophysiol 2005, 116(3):569-587.
• [10]Pfurtscheller G, Da Silva FH L: Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 1999, 110(11):1842-1857.
• [11]Bashashati A, Fatourechi M, Ward RK, Birch GE: A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J Neural Eng 2007, 4(2):R32-R57.
• [12]Liao X, Yao DH, Wu D, Li CY: Combining spatial filters for the classification of, single-trial EEG in a finger movement task. IEEE Trans Biomed Eng 2007, 54(5):821-831.
• [13]Muller-Gerking J, Pfurtscheller G, Flyvbjerg H: Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin Neurophysiol 1999, 110(5):787-798.
• [14]Blankertz B, Dornhege G, Krauledat M, Muller KR, Kunzmann V, Losch F, Curio G: The Berlin brain-computer interface: EEG-based communication without subject training. IEEE Trans Neural Syst Rehabil Eng 2006, 14(2):147-152.
• [15]Kwak N: Principal component analysis based on L1-norm maximization. IEEE Trans Pattern Anal Mach Intell 2008, 30(9):1672-1680.
• [16]Lotte F, Guan CT: Regularizing common spatial patterns to improve BCI designs: unified theory and New algorithms. Ieee Transactions on Biomedical Engineering 2011, 58(2):355-362.
• [17]Lei X, Yang P, Yao DZ: An empirical Bayesian framework for brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 2009, 17(6):521-529.
• [18]Lei X, Yang P, Xu P, Liu T-J, Yao D-Z: Common spatial pattern ensemble classifier and its application in brain-computer interface. J Electronic Science and Technology of China 2009, 7(1):17-21.
• [19]Xu P, Tian Y, Chen HF, Yao DZ: Lp norm iterative sparse solution for EEG source localization. IEEE Trans Biomed Eng 2007, 54(3):400-409.
• [20]Blankertz B, Tomioka R, Lemm S, Kawanabe M, Muller KR: Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag 2008, 25(1):41-56.
• [21]De la Torre F, Black MJ: A framework for robust subspace learning. Int J Comput Vis 2003, 54(1–2):117-142.
• [22]Blankertz B, Muller KR, Krusienski DJ, Schalk G, Wolpaw JR, Schlogl A, Pfurtscheller G, Millan JDR, Schroder M, Birbaumer N: The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng 2006, 14(2):153-159.
• [23]Dornhege G, Blankertz B, Curio G, Muller K: Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms. Biomedical Engineering, IEEE Transactions 2004, 51(6):993-1002.
• [24]Wang HX, Tang Q, Zheng WM: L1-Norm-based common spatial patterns. IEEE Trans Biomed Eng 2012, 59(3):653-662.
• [25]Dietterich TG: Approximate statistical tests for comparing supervised classification learning algorithms. Neural computation 1998, 10(7):1895-1923.
• [26]Xu P, Yang P, Lei X, Yao D: An enhanced probabilistic LDA for multi-class brain computer interface. PLoS One 2011, 6(1):e14634.
• [27]Xu P, Lei X, Hu X, Yao D: Solving of L0 norm constrained EEG inverse problem. Proc IEEE Eng Med Biol Soc 2009.
• [28]Xu P, Tian Y, Lei X, Yao DZ: Neuroelectric source imaging using 3SCO: a space coding algorithm based on particle swarm optimization and l(0) norm constraint. Neuroimage 2010, 51(1):183-205.