Channel selection for optimizing feature extraction in an electrocorticogram-based brain-computer interface.
Feature extractor and classifier are two major components in a brain-computer interface system, in which the feature extractor plays a critical role. To increase the discriminability of features or feature vectors used for classification, it is necessary to select a suitable number of task-related d...
| Publicado en: | Journal of Clinical Neurophysiology Vol. 27; no. 5; pp. 321 - 328 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
2010 Oct
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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=104927735&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104927735 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07360258 8CL jtl: Journal of Clinical Neurophysiology issn: 07360258 maglogo: N pubinfo: dt: 2010 Oct vid: 27 iid: 5 pid: 5086 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 104927735 104927735 2010807626 10.1097/WNP.0b013e3181f52f2d NLM20844441 104927735 ppf: 321 ppct: 7 formats: tig: atl: Channel selection for optimizing feature extraction in an electrocorticogram-based brain-computer interface. aug: au: Wei Q Lu Z Chen K Ma Y affil: From the Department of Electronic Engineering, Nanchang University, Nanchang, China. sug: subj: Artificial Intelligence Brain-Computer Interfaces Cerebral Cortex Physiology Electroencephalography Methods Algorithms Discriminant Analysis Epilepsy, Partial Physiopathology Epilepsy, Partial Surgery Human ab: Feature extractor and classifier are two major components in a brain-computer interface system, in which the feature extractor plays a critical role. To increase the discriminability of features or feature vectors used for classification, it is necessary to select a suitable number of task-related data recording channels. In this article, a machine-learning algorithm is proposed for optimizing feature extraction in an electrocorticogram-based brain-computer interface. Common spatial pattern was used for feature extraction, and channel selection was performed by genetic algorithm for optimizing the feature extraction. Fisher discriminant analysis was used as classifier, and the channel subset chosen at each generation was evaluated by classification accuracy. The algorithm was applied to three electrocorticogram datasets that were recorded during two kinds of motor imagery tasks. The results suggest that the channel number used for building a brain-computer interface system could be significantly decreased without losing classification accuracy, and the accuracy rate could be noticeably improved by using the optimal channel subsets chosen by genetic algorithm. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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