Discriminative Feature Extraction via Multivariate Linear Regression for SSVEP-Based BCI

Haiqiang Wang, Yu Zhang, Nicholas R. Waytowich, Dean J. Krusienski, Guoxu Zhou, Jing Jin, Xingyu Wang, Andrzej Cichocki

    Research output: Contribution to journalArticlepeer-review

    115 Citations (Scopus)


    Many of the most widely accepted methods for reliable detection of steady-state visual evoked potentials (SSVEPs) in the electroencephalogram (EEG) utilize canonical correlation analysis (CCA). CCA uses pure sine and cosine reference templates with frequencies corresponding to the visual stimulation frequencies. These generic reference templates may not optimally reflect the natural SSVEP features obscured by the background EEG. This paper introduces a new approach that utilizes spatio-temporal feature extraction with multivariate linear regression (MLR) to learn discriminative SSVEP features for improving the detection accuracy. MLR is implemented on dimensionality-reduced EEG training data and a constructed label matrix to find optimally discriminative subspaces. Experimental results show that the proposed MLR method significantly outperforms CCA as well as several other competing methods for SSVEP detection, especially for time windows shorter than 1 second. This demonstrates that the MLR method is a promising new approach for achieving improved real-time performance of SSVEP-BCIs.

    Original languageEnglish
    Article number7389413
    Pages (from-to)532-541
    Number of pages10
    JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
    Issue number5
    Publication statusPublished - May 2016


    • Brain-computer interface (BCI)
    • canonical correlation analysis (CCA)
    • electroencephalogram (EEG)
    • multivariate linear regression (MLR)
    • steady-state visual evoked potential (SSVEP)


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