Model based generalization analysis of common spatial pattern in brain computer interfaces

Gan Huang, Guangquan Liu, Jianjun Meng, Dingguo Zhang, Xiangyang Zhu

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


In the motor imagery based Brain Computer Interface (BCI) research, Common Spatial Pattern (CSP) algorithm is used widely as a spatial filter on multi-channel electroencephalogram (EEG) recordings. Recently the overfitting effect of CSP has been gradually noticed, but what influence the overfitting is still unclear. In this work, the generalization of CSP is investigated by a simple linear mixing model. Several factors in this model are discussed, and the simulation results indicate that channel numbers and the correlation between signals influence the generalization of CSP significantly. A larger number of training trials and a longer time length of the trial would prevent overfitting. The experiments on real data also verify our conclusion.

Original languageEnglish (US)
Pages (from-to)217-223
Number of pages7
JournalCognitive Neurodynamics
Issue number3
StatePublished - Sep 2010

Bibliographical note

Funding Information:
Acknowledgments This work is supported by National High Technology Research and Development Program of China (No. 2009AA04Z212), Research Fund for the Doctoral Program of Higher Education of China (No. 20090073120047), and SJTU Interdisciplinary Fund in Medicine & Engineering (No. YG2009MS45).


  • Brain Computer Interfaces
  • Common Spatial Pattern
  • Generalization

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