Relevant Feature Selection from a Combination of Spectral-Temporal and Spatial Features for Classification of Motor Imagery EEG.
This paper presents a novel algorithm (CVSTSCSP) for determining discriminative features from an optimal combination of temporal, spectral and spatial information for motor imagery brain computer interfaces. The proposed method involves four phases. In the first phase, EEG signal is segmented into o...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 5; pp. 1 - 2 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2018
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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=129370610&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129370610 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: May2018 vid: 42 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 129370610 129370610 129370610 10.1007/s10916-018-0931-8 129370610 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Relevant Feature Selection from a Combination of Spectral-Temporal and Spatial Features for Classification of Motor Imagery EEG. aug: au: Kirar, Jyoti Singh Agrawal, R. K. affil: Jawaharlal Nehru University, 110067, New Delhi, India sug: subj: Electroencephalography Classification Motor Activity Brain-Computer Interfaces Human Funding Source Algorithms Experimental Studies P-Value Time Factors ab: This paper presents a novel algorithm (CVSTSCSP) for determining discriminative features from an optimal combination of temporal, spectral and spatial information for motor imagery brain computer interfaces. The proposed method involves four phases. In the first phase, EEG signal is segmented into overlapping time segments and bandpass filtered through frequency filter bank of variable size subbands. In the next phase, features are extracted from the segmented and filtered data using stationary common spatial pattern technique (SCSP) that can handle the non- stationarity and artifacts of EEG signal. The univariate feature selection method is used to obtain a relevant subset of features in the third phase. In the final phase, the classifier is used to build adecision model. In this paper, four univariate feature selection methods such as Euclidean distance, correlation, mutual information and Fisher discriminant ratio and two well-known classifiers (LDA and SVM) are investigated. The proposed method has been validated using the publicly available BCI competition IV dataset Ia and BCI Competition III dataset IVa. Experimental results demonstrate that the proposed method significantly outperforms the existing methods in terms of classification error. A reduction of 76.98%, 75.65%, 73.90% and 72.21% in classification error over both datasets and both classifiers can be observed using the proposed CVSTSCSP method in comparison to CSP, SBCSP, FBCSP and CVSCSP respectively. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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