Common Spatial Pattern Reformulated for Regularizations in Brain-Computer Interfaces

Boyu Wang, Chi Man Wong, Zhao Kang, Feng Liu, Changjian Shui, Feng Wan, C. L.Philip Chen

    Research output: Contribution to journalArticlepeer-review

    42 Scopus citations

    Abstract

    Common spatial pattern (CSP) is one of the most successful feature extraction algorithms for brain-computer interfaces (BCIs). It aims to find spatial filters that maximize the projected variance ratio between the covariance matrices of the multichannel electroencephalography (EEG) signals corresponding to two mental tasks, which can be formulated as a generalized eigenvalue problem (GEP). However, it is challenging in principle to impose additional regularization onto the CSP to obtain structural solutions (e.g., sparse CSP) due to the intrinsic nonconvexity and invariance property of GEPs. This article reformulates the CSP as a constrained minimization problem and establishes the equivalence of the reformulated and the original CSPs. An efficient algorithm is proposed to solve this optimization problem by alternately performing singular value decomposition (SVD) and least squares. Under this new formulation, various regularization techniques for linear regression can then be easily implemented to regularize the CSPs for different learning paradigms, such as the sparse CSP, the transfer CSP, and the multisubject CSP. Evaluations on three BCI competition datasets show that the regularized CSP algorithms outperform other baselines, especially for the high-dimensional small training set. The extensive results validate the efficiency and effectiveness of the proposed CSP formulation in different learning contexts.

    Original languageEnglish
    Pages (from-to)5008-5020
    Number of pages13
    JournalIEEE Transactions on Cybernetics
    Volume51
    Issue number10
    DOIs
    StatePublished - 1 Oct 2021

    Keywords

    • Brain-computer interface (BCI)
    • common spatial pattern (CSP)
    • generalized eigenvalue problem (GEP)
    • least squares
    • multitask learning
    • singular value decomposition (SVD)
    • sparse learning
    • transfer learning

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