Nonnegative matrix factorization common spatial pattern in brain machine interface

H. Tsubakida, T. Shiratori, Atsushi Ishiyama, Y. Ono

    研究成果: Conference contribution

    2 被引用数 (Scopus)

    抄録

    Fast and accurate discrimination of Electroencephalography (EEG) data is necessary for controlling brain machine interface. This paper introduces a novel method to discriminate 2-class motor imagery states (left and right hand) using nonnegative matrix factorization (NMF), common spatial pattern (CSP) and random forest. Conventionally CSP is used after extracting frequency band segment of EEG signal, which is called bandpass-filtered CSP (BPCSP). Especially filter bank CSP (FBCSP) has been extensively used to extract feature vectors from EEG data. However in these methods, the range of frequency band needed to be specified in advance and the performance depends on the selected frequency band. Our new method can decide the frequency band automatically by using NMF (NMFCSP). After the feature vectors were extracted from EEG data, random forests (RF) method was adopted as a classification algorithm. The mean accuracy rate of 2-class classifier using NMFCSP was 78.8±3.27%. This is higher than the accuracy rate of BPCSP (64.4±8.53%) and FBCSP (68.4±6.81%).

    本文言語English
    ホスト出版物のタイトル3rd International Winter Conference on Brain-Computer Interface, BCI 2015
    出版社Institute of Electrical and Electronics Engineers Inc.
    ISBN(印刷版)9781479974948
    DOI
    出版ステータスPublished - 2015 3 30
    イベント2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 - Gangwon-Do, Korea, Republic of
    継続期間: 2015 1 122015 1 14

    Other

    Other2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015
    国/地域Korea, Republic of
    CityGangwon-Do
    Period15/1/1215/1/14

    ASJC Scopus subject areas

    • 人間とコンピュータの相互作用
    • 認知神経科学
    • 感覚系

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