Comparison of feature selection and classification methods for a brain-computer interface driven by non-motor imagery.
The aim of this study was to compare methods for feature extraction and classification of EEG signals for a brain-computer interface (BCI) driven by auditory and spatial navigation imagery. Features were extracted using autoregressive modeling and optimized discrete wavelet transform. The features w...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 48; no. 2; pp. 123 - 133 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Feb2010
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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=104908404&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104908404 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Feb2010 vid: 48 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104908404 104908404 NLM20041311 2010538114 10.1007/s11517-009-0569-2 NLM20041311 104908404 ppf: 123 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Comparison of feature selection and classification methods for a brain-computer interface driven by non-motor imagery. aug: au: Cabrera AF Farina D Dremstrup K Cabrera, Alvaro Fuentes Farina, Dario Dremstrup, Kim affil: Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark sug: subj: Brain Physiology Brain-Computer Interfaces Perception Adult Communication Aids for Persons with Disabilities Electroencephalography Methods Female Male Signal Processing, Computer Assisted Young Adult Adult: 19-44 years Female Male ab: The aim of this study was to compare methods for feature extraction and classification of EEG signals for a brain-computer interface (BCI) driven by auditory and spatial navigation imagery. Features were extracted using autoregressive modeling and optimized discrete wavelet transform. The features were selected with exhaustive search, from the combination of features of two and three channels, and with a discriminative measure (r (2)). Moreover, Bayesian classifier and support vector machine (SVM) with Gaussian kernel were compared. The results showed that the two classifiers provided similar classification accuracy. Conversely, the exhaustive search of the optimal combination of features from two and three channels significantly improved performance with respect to using r(2) for channel selection. With features optimally extracted from three channels with optimized scaling filter in the discrete wavelet transform, the classification accuracy was on average 72.2%. Thus, the choice of features had greater impact on performance than the choice of the classifier for discrimination between the two non-motor imagery tasks investigated. The results are relevant for the choice of the translation algorithm for an on-line BCI system based on non-motor imagery. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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