A spatial-frequency-temporal optimized feature sparse representation-based classification method for motor imagery EEG pattern recognition.
Effective feature extraction and classification methods are of great importance for motor imagery (MI)-based brain-computer interface (BCI) systems. The common spatial pattern (CSP) algorithm is a widely used feature extraction method for MI-based BCIs. In this work, we propose a novel spatial-frequ...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 9; pp. 1589 - 1604 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124786147&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124786147 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2017 vid: 55 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 124786147 124786147 144052886 NLM28161876 10.1007/s11517-017-1622-1 NLM28161876 124786147 ppf: 1589 ppct: 15 formats: fmt: @attributes: type: P tig: atl: A spatial-frequency-temporal optimized feature sparse representation-based classification method for motor imagery EEG pattern recognition. aug: au: Miao, Minmin Wang, Aimin Liu, Feixiang affil: School of Instrument Science and Engineering , Southeast University , No. 2 Sipailou Nanjing 210096 China sug: subj: Electroencephalography Methods Information Science Methods Guided Imagery Methods Brain-Computer Interfaces Signal Processing, Computer Assisted Equipment and Supplies Imagination Algorithms Brain Physiology Scales ab: Effective feature extraction and classification methods are of great importance for motor imagery (MI)-based brain-computer interface (BCI) systems. The common spatial pattern (CSP) algorithm is a widely used feature extraction method for MI-based BCIs. In this work, we propose a novel spatial-frequency-temporal optimized feature sparse representation-based classification method. Optimal channels are selected based on relative entropy criteria. Significant CSP features on frequency-temporal domains are selected automatically to generate a column vector for sparse representation-based classification (SRC). We analyzed the performance of the new method on two public EEG datasets, namely BCI competition III dataset IVa which has five subjects and BCI competition IV dataset IIb which has nine subjects. Compared to the performance offered by the existing SRC method, the proposed method achieves average classification accuracy improvements of 21.568 and 14.38% for BCI competition III dataset IVa and BCI competition IV dataset IIb, respectively. Furthermore, our approach also shows better classification performance when compared to other competing methods for both datasets. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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