Investigation of different classifiers and channel configurations of a mobile P300-based brain-computer interface.
Innovative methods and new technologies have significantly improved the quality of our daily life. However, disabled people, for example those that cannot use their arms and legs anymore, often cannot benefit from these developments, since they cannot use their hands to interact with traditional int...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 12; pp. 2143 - 2155 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2017
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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=126132866&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 126132866 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2017 vid: 55 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 126132866 126132866 144169147 NLM28553694 126132866 10.1007/s11517-017-1658-2 NLM28553694 126132866 ppf: 2143 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Investigation of different classifiers and channel configurations of a mobile P300-based brain-computer interface. aug: au: Ludwig, Simone Kong, Jun Ludwig, Simone A affil: Department of Computer Science , North Dakota State University , Fargo USA sug: subj: Brain-Computer Interfaces Electroencephalography Classification Signal Processing, Computer Assisted Evoked Potentials Physiology Male Young Adult Female Discriminant Analysis Adult Human Adult: 19-44 years Male Female ab: Innovative methods and new technologies have significantly improved the quality of our daily life. However, disabled people, for example those that cannot use their arms and legs anymore, often cannot benefit from these developments, since they cannot use their hands to interact with traditional interaction methods (such as mouse or keyboard) to communicate with a computer system. A brain-computer interface (BCI) system allows such a disabled person to control an external device via brain waves. Past research mostly dealt with static interfaces, which limit users to a stationary location. However, since we are living in a world that is highly mobile, this paper evaluates a speller interface on a mobile phone used in a moving condition. The spelling experiments were conducted with 14 able-bodied subjects using visual flashes as the stimulus to spell 47 alphanumeric characters (38 letters and 9 numbers). This data was then used for the classification experiments. In par- ticular, two research directions are pursued. The first investigates the impact of different classification algorithms, and the second direction looks at the channel configuration, i.e., which channels are most beneficial in terms of achieving the highest classification accuracy. The evaluation results indicate that the Bayesian Linear Discriminant Analysis algorithm achieves the best accuracy. Also, the findings of the investigation on the channel configuration, which can potentially reduce the amount of data processing on a mobile device with limited computing capacity, is especially useful in mobile BCIs. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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