Single-Trial EEG Classification via Common Spatial Patterns with Mixed Lp- and Lq-Norms

As a multichannel spatial filtering technique, common spatial patterns (CSP) have been successfully applied in brain-computer interfaces (BCI) community based on electroencephalogram (EEG). However, it is sensitive to outliers because of the employment of the L2-norm in its formulation. It is beneficial to perform robust modelling for CSP. In this paper, we propose a robust framework, called CSP-Lp/q, by formulating the variances of two EEG classes with Lp- and Lq-norms ( 0 < p and q < 2 ) separately. The method CSP-Lp/q with mixed Lp- and Lq-norms takes the class-wise difference into account in formulating the sample dispersion. We develop an iterative algorithm to optimize the objective function of CSP-Lp/q and show its monotonity theoretically. The superiority of the proposed CSP-Lp/q technique is experimentally demonstrated on three real EEG datasets of BCI competitions.

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Speeding up classification of multi-channel brain-computer interfaces: common spatial patterns for slow cortical potentials

First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings. ◽

10.1109/cne.2003.1196898 ◽

2004 ◽

Cited By ~ 19

Author(s):

G. Dornhege ◽

B. Blankertz ◽

G. Curio

Keyword(s):

Spatial Patterns ◽

Brain Computer Interfaces ◽

Slow Cortical Potentials ◽

Common Spatial Patterns ◽

Computer Interfaces ◽

Cortical Potentials

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Multi-view Optimization of Time-frequency Common Spatial Patterns for Brain-computer Interfaces

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2021.109378 ◽

2021 ◽

pp. 109378

Author(s):

Yitao Huang ◽

Jing Jin ◽

Ren Xu ◽

Yangyang Miao ◽

Chang Liu ◽

...

Keyword(s):

Spatial Patterns ◽

Brain Computer Interfaces ◽

Time Frequency ◽

Common Spatial Patterns ◽

Computer Interfaces

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Correlation Assisted Strong Uncorrelating Transform Complex Common Spatial Patterns for Spatially Distant Channel Data

Computational Intelligence and Neuroscience ◽

10.1155/2018/4281230 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9

Author(s):

Youngjoo Kim ◽

Jiwoo You ◽

Heejun Lee ◽

Seung Min Lee ◽

Cheolsoo Park

Keyword(s):

Spatial Patterns ◽

Rank Test ◽

Simultaneous Diagonalization ◽

Common Spatial Patterns ◽

Average Classification Accuracy ◽

Signed Rank ◽

Multichannel Data ◽

Signed Rank Test ◽

Correlation Information ◽

Electroencephalogram Eeg

The Strong Uncorrelating Transform Complex Common Spatial Patterns (SUTCCSP) algorithm, designed for multichannel data analysis, has a limitation on keeping the correlation information among channels during the simultaneous diagonalization process of the covariance and pseudocovariance matrices. This paper focuses on the importance of preserving the correlation information among multichannel data and proposes the correlation assisted SUTCCSP (CASUT) algorithm to address this issue. The performance of the proposed algorithm was demonstrated by classifying the motor imagery electroencephalogram (EEG) dataset. The features were first extracted using CSP algorithms including the proposed method, and then the random forest classifier was utilized for the classification. Experiments using CASUT yielded an average classification accuracy of 78.10 (%), which significantly outperformed those of original CSP, Complex Common Spatial Patterns (CCSP), and SUTCCSP with p-values less than 0.01, tested by the Wilcoxon signed rank test.

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