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

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

Standard

Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training. / Bender, Thomas; Kjaer, Troels W.; Thomsen, Carsten E.; Sorensen, Helge B D; Puthusserypady, S.

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 › fagfællebedømt

Harvard

Bender, T, Kjaer, TW, Thomsen, CE, Sorensen, HBD & Puthusserypady, S 2013, Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training. i Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS., 6610491, s. 4279-4282. https://doi.org/10.1109/EMBC.2013.6610491

APA

Bender, T., Kjaer, T. W., Thomsen, C. E., Sorensen, H. B. D., & Puthusserypady, S. (2013). Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training. I Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (s. 4279-4282). [6610491] https://doi.org/10.1109/EMBC.2013.6610491

Vancouver

Bender T, Kjaer TW, Thomsen CE, Sorensen HBD, Puthusserypady S. Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training. I Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2013. s. 4279-4282. 6610491 https://doi.org/10.1109/EMBC.2013.6610491

Author

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

Bibtex

@inproceedings{d1c6bf690d7c481e98d27bae66def781,
title = "Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training",
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.",
keywords = "Autocorrelation, Brain-Computer Interface, Na{\"i}ve-Bayes Classifier, Steady-State Visual Evoked Potentials, Tri-training",
author = "Thomas Bender and Kjaer, {Troels W.} and Thomsen, {Carsten E.} and Sorensen, {Helge B D} and S. Puthusserypady",
year = "2013",
month = oct,
day = "31",
doi = "10.1109/EMBC.2013.6610491",
language = "English",
isbn = "9781457702167",
pages = "4279--4282",
booktitle = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

}

RIS

TY - GEN

T1 - Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

AU - Bender, Thomas

AU - Kjaer, Troels W.

AU - Thomsen, Carsten E.

AU - Sorensen, Helge B D

AU - Puthusserypady, S.

PY - 2013/10/31

Y1 - 2013/10/31

N2 - 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.

AB - 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.

KW - Autocorrelation

KW - Brain-Computer Interface

KW - Naïve-Bayes Classifier

KW - Steady-State Visual Evoked Potentials

KW - Tri-training

U2 - 10.1109/EMBC.2013.6610491

DO - 10.1109/EMBC.2013.6610491

M3 - Article in proceedings

C2 - 24110678

AN - SCOPUS:84886483574

SN - 9781457702167

SP - 4279

EP - 4282

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

ER -

ID: 120786973