Feature selection on movement imagery discrimination and attention detection.
Noninvasive brain-computer interfaces (BCI) translate subject's electroencephalogram (EEG) features into device commands. Large feature sets should be down-selected for efficient feature translation. This work proposes two different feature down-selection algorithms for BCI: (a) a sequential forward...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 48; no. 4; pp. 331 - 342 |
|---|---|
| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Apr2010
|
| 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=104909075&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104909075 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2010 vid: 48 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104909075 NLM20112135 2010595635 10.1007/s11517-010-0578-1 NLM20112135 PMC2946110 104909075 ppf: 331 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Feature selection on movement imagery discrimination and attention detection. aug: au: Dias NS Kamrunnahar M Mendes PM Schiff SJ Correia JH Dias, N S Kamrunnahar, M Mendes, P M Schiff, S J Correia, J H affil: Department of Industrial Electronics, University of Minho, Guimaraes, Portugal sug: subj: Attention Imagination Movement Physiology User-Computer Interface Adult Algorithms Brain Physiology Discrimination Electroencephalography Methods Evoked Potentials Physiology Female Male Adult: 19-44 years Female Male ab: Noninvasive brain-computer interfaces (BCI) translate subject's electroencephalogram (EEG) features into device commands. Large feature sets should be down-selected for efficient feature translation. This work proposes two different feature down-selection algorithms for BCI: (a) a sequential forward selection; and (b) an across-group variance. Power rar ratios (PRs) were extracted from the EEG data for movement imagery discrimination. Event-related potentials (ERPs) were employed in the discrimination of cue-evoked responses. While center-out arrows, commonly used in calibration sessions, cued the subjects in the first experiment (for both PR and ERP analyses), less stimulating arrows that were centered in the visual field were employed in the second experiment (for ERP analysis). The proposed algorithms outperformed other three popular feature selection algorithms in movement imagery discrimination. In the first experiment, both algorithms achieved classification errors as low as 12.5% reducing the feature set dimensionality by more than 90%. The classification accuracy of ERPs dropped in the second experiment since centered cues reduced the amplitude of cue-evoked ERPs. The two proposed algorithms effectively reduced feature dimensionality while increasing movement imagery discrimination and detected cue-evoked ERPs that reflect subject attention. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|