Task-free brainprint recognition based on low-rank and sparse decomposition model

Wanzeng Kong, Xianghao Kong, Qiaonan Fan, Qibin Zhao, Andrzej Cichocki

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)

    Abstract

    Electroencephalography (EEG)-based brainprint recognition was usually completed under a singular task, such as recognition based on visual-evoked potentials. This paper proposes a fast task-free brainprint recognition to break the restriction. We presume a task-related EEG can be divided into the background EEG (BEEG) and the residue EEG. Wherein, BEEG contains one's unique intrinsic brainprint, which was supposed to be a low-rank characteristic. To analyse more precisely, short time Fourier Transform (STFT) are exerted to expand time series EEG into time-frequency domain. Then, a Low-Rank Matrix Decomposition (LRMD)-based algorithm combined with maximum correntropy criterion (MCC) and rational quadratic kernel was designed to extract BEEG. Finally, through sparse representation, BEEG can be classified efficiently. The excellent performance under low rank and various time length scales indicates that our method does not rely on task types and provides a new direction for the application of brainprint recognition.

    Original languageEnglish
    Pages (from-to)280-300
    Number of pages21
    JournalInternational Journal of Data Mining and Bioinformatics
    Volume22
    Issue number3
    DOIs
    Publication statusPublished - 2019

    Keywords

    • Background EEG
    • Brainprint
    • Maximum correntropy criterion
    • Rational quadratic kernel
    • Sparse representation
    • Task-free

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