An effective feature extraction method by power spectral density of EEG signal for 2-class motor imagery-based BCI.
EEG signals have weak intensity, low signal-to-noise ratio, non-stationary, non-linear, time-frequency-spatial characteristics. Therefore, it is important to extract adaptive and robust features that reflect time, frequency and spatial characteristics. This paper proposes an effective feature extrac...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 9; pp. 1645 - 1659 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2018
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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=131278127&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131278127 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2018 vid: 56 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131278127 131278127 NLM29497931 10.1007/s11517-017-1761-4 NLM29497931 131278127 ppf: 1645 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An effective feature extraction method by power spectral density of EEG signal for 2-class motor imagery-based BCI. aug: au: Kim, Chungsong Sun, Jinwei Liu, Dan Wang, Qisong Paek, Sunggyun affil: School of Electrical Engineering & Automation, Harbin Institute of Technology, 92 West Dazhi Street, 150001, Harbin, People’s Republic of China sug: subj: Guided Imagery Brain-Computer Interfaces Signal Processing, Computer Assisted Electroencephalography Algorithms Databases ab: EEG signals have weak intensity, low signal-to-noise ratio, non-stationary, non-linear, time-frequency-spatial characteristics. Therefore, it is important to extract adaptive and robust features that reflect time, frequency and spatial characteristics. This paper proposes an effective feature extraction method WDPSD (feature extraction from the Weighted Difference of Power Spectral Density in an optimal channel couple) that can reflect time, frequency and spatial characteristics for 2-class motor imagery-based BCI system. In the WDPSD method, firstly, Power Spectral Density (PSD) matrices of EEG signals are calculated in all channels, and an optimal channel couple is selected from all possible channel couples by checking non-stationary and class separability, and then a weight matrix which reflects non-stationary of PSD difference matrix in selected channel couple is calculated; finally, the robust and adaptive features are extracted from the PSD difference matrix weighted by the weight matrix. The proposed method is evaluated from EEG signals of BCI Competition IV Dataset 2a and Dataset 2b. The experimental results show a good classification accuracy in single session, session-to-session, and the different types of 2-class motor imagery for different subjects. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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