Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

Thomas Bender; Troels W. Kjaer; Carsten E. Thomsen; Helge B D Sorensen; S. Puthusserypady

doi:10.1109/embc.2013.6610491

Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

Thomas Bender, Troels W. Kjaer, Carsten E. Thomsen, Helge B D Sorensen, S. Puthusserypady

3 Citationer (Scopus)

Abstract

This paper presents a novel and computationally simple tri-training based semi-supervised steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). It is implemented with autocorrelation-based features and a Naïve-Bayes classifier (NBC). The system uses nine characters presented on a 100 Hz CRT-monitor, three scalp electrodes for signal acquisition, a gUSB-amp for preamplification and two PCs for data-processing and stimulus control respectively. Preliminary test results of the system on nine healthy subjects, with and without tri-training, indicates that the accuracy improves as a result of tri-training.

Originalsprog	Engelsk
Titel	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Antal sider	4
Publikationsdato	31 okt. 2013
Sider	4279-4282
Artikelnummer	6610491
ISBN (Trykt)	9781457702167
DOI	https://doi.org/10.1109/embc.2013.6610491
Status	Udgivet - 31 okt. 2013

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10.1109/embc.2013.6610491

http://www.scopus.com/inward/record.url?scp=84886483574&partnerID=8YFLogxK

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Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training. / Bender, Thomas; Kjaer, Troels W.; Thomsen, Carsten E. et al.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2013. s. 4279-4282 6610491.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

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abstract = "This paper presents a novel and computationally simple tri-training based semi-supervised steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). It is implemented with autocorrelation-based features and a Na{\"i}ve-Bayes classifier (NBC). The system uses nine characters presented on a 100 Hz CRT-monitor, three scalp electrodes for signal acquisition, a gUSB-amp for preamplification and two PCs for data-processing and stimulus control respectively. Preliminary test results of the system on nine healthy subjects, with and without tri-training, indicates that the accuracy improves as a result of tri-training.",

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AU - Puthusserypady, S.

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KW - Brain-Computer Interface

KW - Naïve-Bayes Classifier

KW - Steady-State Visual Evoked Potentials

KW - Tri-training

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BT - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

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Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

Abstract

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