Task-Independent EEG Identification via Low-Rank Matrix Decomposition

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

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    12 Citations (Scopus)

    Abstract

    With advantages of high concealment, non-stealing, and liveness detection, electroencephalography (EEG) identification has a broad application prospect in the fields with high confidentiality and security requirements. At present, EEG identification usually requires external stimuli or particular tasks imposed on participators, such as identification based on movement imagination (MI) and event-related potential (ERP), which restricts its promotion in real life. To overcome the limitation, we assume a task-related EEG can be divided into a background EEG (BEEG) containing one's unique intrinsic features and a residue EEG (REEG) composed of task-evoked EEG and random noise. Furthermore, we suppose only a few features can reveal one's identity. Therefore, BEEG represents a low-rank characteristic, suitable for personal identification. In this paper, we proposed a fast LRMD-based EEG identification algorithm with maximum correntropy criterion (MCC) and rational quadratic kernel, which can efficiently extract BEEG out and deliver a high accuracy classification. Extensive experiments conducted on three public EEG datasets and a self-collected multi-task EEG dataset all achieve outstanding performance under the low rank of BEEG data matrix and various time length scales of short-time Fourier Transform (STFT), which means that our approach does not depend on the task type. Besides, the experimental results provide a reference for the time length of an appropriate EEG signal sample.

    Original languageEnglish
    Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
    EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages412-419
    Number of pages8
    ISBN (Electronic)9781538654880
    DOIs
    Publication statusPublished - 21 Jan 2019
    Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
    Duration: 3 Dec 20186 Dec 2018

    Publication series

    NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

    Conference

    Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
    Country/TerritorySpain
    CityMadrid
    Period3/12/186/12/18

    Keywords

    • Background EEG (BEEG)
    • maximum Correntropy Criterion (MCC)
    • rational quadratic kernel
    • sparse Representation (SR)
    • task-independent identification

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