Predictive classification of self-paced upper-limb analytical movements with EEG.
The extent to which the electroencephalographic activity allows the characterization of movements with the upper limb is an open question. This paper describes the design and validation of a classifier of upper-limb analytical movements based on electroencephalographic activity extracted from interv...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 53; no. 11; pp. 1201 - 1211 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Nov2015
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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=110654126&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110654126 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2015 vid: 53 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 110654126 110654126 NLM25980505 110654126 10.1007/s11517-015-1311-x NLM25980505 110654126 ppf: 1201 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Predictive classification of self-paced upper-limb analytical movements with EEG. aug: au: Ibáñez, Jaime Serrano, J. Castillo, M. Minguez, J. Pons, J. Ibáñez, Jaime Serrano, J I Del Castillo, M D Pons, J L affil: Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council - CSIC, Av. Doctor Arce, 37 28002 Madrid Spain sug: subj: Upper Extremity Physiology Signal Processing, Computer Assisted Electroencephalography Methods Reproducibility of Results Algorithms Male Adult Human Adult: 19-44 years Male ab: The extent to which the electroencephalographic activity allows the characterization of movements with the upper limb is an open question. This paper describes the design and validation of a classifier of upper-limb analytical movements based on electroencephalographic activity extracted from intervals preceding self-initiated movement tasks. Features selected for the classification are subject specific and associated with the movement tasks. Further tests are performed to reject the hypothesis that other information different from the task-related cortical activity is being used by the classifiers. Six healthy subjects were measured performing self-initiated upper-limb analytical movements. A Bayesian classifier was used to classify among seven different kinds of movements. Features considered covered the alpha and beta bands. A genetic algorithm was used to optimally select a subset of features for the classification. An average accuracy of 62.9 ± 7.5% was reached, which was above the baseline level observed with the proposed methodology (30.2 ± 4.3%). The study shows how the electroencephalography carries information about the type of analytical movement performed with the upper limb and how it can be decoded before the movement begins. In neurorehabilitation environments, this information could be used for monitoring and assisting purposes. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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