Detection of movement-related cortical potentials based on subject-independent training.
To allow a routinely use of brain-computer interfaces (BCI), there is a need to reduce or completely eliminate the time-consuming part of the individualized training of the user. In this study, we investigate the possibility of avoiding the individual training phase in the detection of movement inte...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 51; no. 5; pp. 507 - 513 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
May2013
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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=109855809&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109855809 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2013 vid: 51 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109855809 NLM23283643 2012084095 10.1007/s11517-012-1018-1 NLM23283643 PMC3627050 109855809 ppf: 507 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Detection of movement-related cortical potentials based on subject-independent training. aug: au: Niazi, Imran Khan Jiang, Ning Jochumsen, Mads Nielsen, Jørgen Feldbæk Dremstrup, Kim Farina, Dario affil: Department of Health Science and Technology, Center for Sensory-Motor Interaction, Aalborg University, Aalborg, Denmark. sug: subj: Brain-Computer Interfaces Cerebral Cortex Physiology Evoked Potentials Physiology Movement Physiology Stroke Physiopathology Adolescence Adult Aged Algorithms Ankle Joint Physiology Electroencephalography Methods Female Human Imagination Learning Male Middle Age Signal Processing, Computer Assisted Young Adult Adolescent: 13-18 years Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years Female Male ab: To allow a routinely use of brain-computer interfaces (BCI), there is a need to reduce or completely eliminate the time-consuming part of the individualized training of the user. In this study, we investigate the possibility of avoiding the individual training phase in the detection of movement intention in asynchronous BCIs based on movement-related cortical potential (MRCP). EEG signals were recorded during ballistic ankle dorsiflexions executed (ME) or imagined (MI) by 20 healthy subjects, and attempted by five stroke subjects. These recordings were used to identify a template (as average over all subjects) for the initial negative phase of the MRCPs, after the application of an optimized spatial filtering used for pre-processing. Using this template, the detection accuracy (mean ± SD) calculated as true positive rate (estimated with leave-one-out procedure) for ME was 69 ± 21 and 58 ± 11 % on single trial basis for healthy and stroke subjects, respectively. This performance was similar to that obtained using an individual template for each subject, which led to accuracies of 71 ± 6 and 55 ± 12 % for healthy and stroke subjects, respectively. The detection accuracy for the MI data was 65 ± 22 % with the average template and 60 ± 13 % with the individual template. These results indicate the possibility of detecting movement intention without an individual training phase and without a significant loss in performance. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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