Improvement Motor Imagery EEG Classification Based on Regularized Linear Discriminant Analysis.
Mental tasks classification such as motor imagery, based on EEG signals is an important problem in brain computer interface systems (BCI). One of the major concerns in BCI is to have a high classification accuracy. The other concerning one is with the favorable result is guaranteed how to improve th...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 6; pp. 1 - 14 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas review tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2019
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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=136503255&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136503255 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jun2019 vid: 43 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136503255 136503255 136503255 10.1007/s10916-019-1270-0 136503255 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improvement Motor Imagery EEG Classification Based on Regularized Linear Discriminant Analysis. aug: au: Fu, Rongrong Tian, Yongsheng Bao, Tiantian Meng, Zong Shi, Peiming affil: Key Lab of Measurement Technology & Instrumentation of Hebei Province, Yanshan University, 066004, Qinhuangdao, China sug: subj: Motor Activity Physiology Electroencephalography Classification Brain-Computer Interfaces Guided Imagery Methods Dimensionality Reduction Algorithms ROC Curve ab: Mental tasks classification such as motor imagery, based on EEG signals is an important problem in brain computer interface systems (BCI). One of the major concerns in BCI is to have a high classification accuracy. The other concerning one is with the favorable result is guaranteed how to improve the computational efficiency. In this paper, Mu/Beta rhythm was obtained by bandpass filter from EEG signal. And the classical linear discriminant analysis (LDA) was used for deciding which rhythm can give the better classification performance. During this, the common spatial pattern (CSP) was used to project data subject to the ratio of projected energy of one class to that of the other class was maximized. The optimal projection dimension was determined corresponding to the maximum of area under the curve (AUC) for each participant. Eventually, regularized linear discriminant analysis (RLDA) is possible to decode the imagined motor sensed using electroencephalogram (EEG). Results show that higher classification accuracy can be provided by RLDA. And optimal projection dimensions determined by LDA and RLDA are of consistent solution, this improves computational efficiency of CSP-RLDA method without computation of projection dimension. pubtype: Academic Journal doctype: equations & formulas review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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