Unsupervised movement onset detection from EEG recorded during self-paced real hand movement.
This article presents an unsupervised method for movement onset detection from electroencephalography (EEG) signals recorded during self-paced real hand movement. A Gaussian Mixture Model (GMM) is used to model the movement and idle-related EEG data. The GMM built along with appropriate classificati...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 48; no. 3; pp. 245 - 254 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Mar2010
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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=104908634&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104908634 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2010 vid: 48 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104908634 NLM19888613 2010564235 10.1007/s11517-009-0550-0 NLM19888613 104908634 ppf: 245 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Unsupervised movement onset detection from EEG recorded during self-paced real hand movement. aug: au: Awwad Shiekh Hasan B Gan JQ Awwad Shiekh Hasan, Bashar Gan, John Q affil: BCI Group, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK sug: subj: Brain Physiology Hand Physiology Movement Physiology Brain Mapping Methods Electroencephalography Methods Female Male Psychomotor Performance Signal Processing, Computer Assisted User-Computer Interface Female Male ab: This article presents an unsupervised method for movement onset detection from electroencephalography (EEG) signals recorded during self-paced real hand movement. A Gaussian Mixture Model (GMM) is used to model the movement and idle-related EEG data. The GMM built along with appropriate classification and post processing methods are used to detect movement onsets using self-paced EEG signals recorded from five subjects, achieving True-False rate difference between 63 and 98%. The results show significant performance enhancement using the proposed unsupervised method, both in the sample-by-sample classification accuracy and the event-by-event performance, in comparison with the state-of-the-art supervised methods. The effectiveness of the proposed method suggests its potential application in self-paced Brain-Computer Interfaces (BCI). pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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