A comparison of feature extraction strategies using wavelet dictionaries and feature selection methods for single trial P300-based BCI.
The P300 component of event-related potentials (ERPs) is widely used in the implementation of brain computer interfaces (BCI). In this context, one of the main issues to solve is the binary classification problem that entails differentiating between electroencephalographic (EEG) signals with and wit...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 3; pp. 589 - 601 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Mar2019
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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=135086826&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135086826 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2019 vid: 57 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135086826 135086826 NLM30267255 135086826 10.1007/s11517-018-1898-9 NLM30267255 135086826 ppf: 589 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A comparison of feature extraction strategies using wavelet dictionaries and feature selection methods for single trial P300-based BCI. aug: au: Acevedo, R. Atum, Y. Gareis, I. Biurrun Manresa, J. Medina Bañuelos, V. Rufiner, L. affil: LIRINS - Facultad de Ingeniería, Universidad Nacional de Entre Rios, Oro Verde, Argentina sug: subj: Brain-Computer Interfaces Evoked Potentials Algorithms Signal Processing, Computer Assisted Human Adult Electroencephalography Methods Resource Databases Validation Studies Comparative Studies Evaluation Research Multicenter Studies Adult: 19-44 years ab: The P300 component of event-related potentials (ERPs) is widely used in the implementation of brain computer interfaces (BCI). In this context, one of the main issues to solve is the binary classification problem that entails differentiating between electroencephalographic (EEG) signals with and without P300. Given the particularly unfavorable signal-to-noise ratio (SNR) in the single-trial detection scenario, this is a challenging problem in the pattern recognition field. To the best of our knowledge, there are no previous experimental studies comparing feature extraction and selection methods for single trial P300-based BCIs using unified criteria and data. In order to improve the performance and robustness of single-trial classifiers, we analyzed and compared different alternatives for the feature generation and feature selection blocks. We evaluated different orthogonal decompositions based on the wavelet transform for feature extraction, as well as different filter, wrapper, and embedded alternatives for feature selection. Accuracies over 75% were obtained for most of the analyzed strategies with a relatively low computational cost, making them attractive for a practical BCI implementation using inexpensive hardware. Graphical Abstract Experiments performed for P300 detection. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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