The Auto-Correlation Function Aided Sparse Support Matrix Machine for EEG-Based Fatigue Detection

Yuxiang Li, Dongqing Wang, Feng Liu

    Research output: Contribution to journalArticlepeer-review

    32 Scopus citations

    Abstract

    Due to support vector machine (SVM) methods generally vectorizing the input matrix and destroying the location dependence characteristic of EEG signals, the support matrix machine (SMM) methods have been presented to incorporate input signal with its location information into the input data matrix and to perform the classification algorithm. In this brief, we investigate an auto-correlation function based sparse support matrix machine (ACF-SSMM) algorithm to optimize and classify the EEG fatigue signals. The main contents include: 1) to input a matrix EEG signal with location information and compress the redundant features by using the sparse principle; 2) to extend the input matrix by adding data through an ACF transform, which takes into account information containing the previous/current instants, to accurately express the memory dynamics of the EEG signals; 3) to perform multiple similar classification algorithms on the EEG fatigue dataset (SEED-VIG dataset) to verify effectiveness of the presented ACF-SSMM algorithm in EEG-based fatigue detection. The experiments get better results, and show the effectiveness of the proposed algorithm.

    Original languageEnglish
    Pages (from-to)836-840
    Number of pages5
    JournalIEEE Transactions on Circuits and Systems II: Express Briefs
    Volume70
    Issue number2
    DOIs
    StatePublished - 1 Feb 2023

    Keywords

    • Support matrix machine
    • auto-correlation function
    • electroencephalogram (EEG)
    • fatigue detection

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