Classification of multichannel EEG patterns using parallel hidden Markov models.
In this paper, a parallel hidden-Markov-model (PHMM)-based approach is proposed for the problem of multichannel electroencephalogram (EEG) patterns classification. The approach is based on multi-channel representation of the EEG signals using a parallel combination of HMMs, where each model represen...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 50; no. 4; pp. 319 - 329 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Apr2012
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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=104545224&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104545224 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2012 vid: 50 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104545224 NLM22407476 2011504887 10.1007/s11517-012-0871-2 NLM22407476 104545224 ppf: 319 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Classification of multichannel EEG patterns using parallel hidden Markov models. aug: au: Lederman D Tabrikian J Lederman, Dror Tabrikian, Joseph affil: Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel sug: subj: Brain Physiology Electroencephalography Methods Signal Processing, Computer Assisted User-Computer Interface Algorithms Human Probability ab: In this paper, a parallel hidden-Markov-model (PHMM)-based approach is proposed for the problem of multichannel electroencephalogram (EEG) patterns classification. The approach is based on multi-channel representation of the EEG signals using a parallel combination of HMMs, where each model represents a particular channel. The performance of the proposed algorithm is studied using an artificial EEG database, and two real EEG databases: a database of two classes of EEGs elicited during a task of imagery of hand upward and downward movements of a computer screen cursor (db Ia), and a database of two classes of sensorimotor EEGs elicited during a feedback-regulated left-right motor imagery task (db III). The results show that the proposed algorithm outperforms other commonly used methods with classification rate improvement of 2 and 10% for db Ia and db III, respectively. In addition, the proposed method outperforms a support vector machine classifier with a linear kernel, when both classifiers utilize the same feature set. The results also show that a model architecture which includes a left-to-right scheme with no skips, five states and three Gaussians, outperforms the other tested architectures due to the fact that it allows a better modeling of the temporal sequencing of the EEG components. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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