A comparison of subject-dependent and subject-independent channel selection strategies for single-trial P300 brain computer interfaces.
Brain computer interfaces (BCI) represent an alternative for patients whose cognitive functions are preserved, but are unable to communicate via conventional means. A commonly used BCI paradigm is based on the detection of event-related potentials, particularly the P300, immersed in the electroencep...
| Published in: | Medical & Biological Engineering & Computing Vol. 57; no. 12; pp. 2705 - 2716 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Dec2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=140034798&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 140034798 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2019 vid: 57 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 140034798 140034798 NLM31728934 10.1007/s11517-019-02065-z NLM31728934 140034798 ppf: 2705 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A comparison of subject-dependent and subject-independent channel selection strategies for single-trial P300 brain computer interfaces. aug: au: Atum, Yanina Pacheco, Marianela Acevedo, Rubén Tabernig, Carolina Biurrun Manresa, José affil: Laboratory for Rehabilitation Engineering and Neuromuscular and Sensory Research (LIRINS), Faculty of Engineering, National University of Entre Ríos (UNER), Route 11 km. 10, 3100, Oro Verde, Argentina sug: subj: Brain Physiology Evoked Potentials Physiology Female Young Adult Algorithms Male Brain-Computer Interfaces Adult Electroencephalography Methods Scales Adult: 19-44 years Female Male ab: Brain computer interfaces (BCI) represent an alternative for patients whose cognitive functions are preserved, but are unable to communicate via conventional means. A commonly used BCI paradigm is based on the detection of event-related potentials, particularly the P300, immersed in the electroencephalogram (EEG). In order to transfer laboratory-tested BCIs into systems that can be used by at homes, it is relevant to investigate if it is possible to select a limited set of EEG channels that work for most subjects and across different sessions without a significant decrease in performance. In this work, two strategies for channel selection for a single-trial P300 brain computer interface were evaluated and compared. The first strategy was tailored specifically for each subject, whereas the second strategy aimed at finding a subject-independent set of channels. In both strategies, genetic algorithms (GAs) and recursive feature elimination algorithms were used. The classification stage was performed using a linear discriminant. A dataset of EEG recordings from 18 healthy subjects was used test the proposed configurations. Performance indexes were calculated to evaluate the system. Results showed that a fixed subset of four subject-independent EEG channels selected using GA provided the best compromise between BCI setup and single-trial system performance. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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