A time domain classification of steady-state visual evoked potentials using deep recurrent-convolutional neural networks

Attia, Mohammed, Hettiarachchi, Imali, Hossny, Mohammed and Nahavandi, Saeid 2018, A time domain classification of steady-state visual evoked potentials using deep recurrent-convolutional neural networks, in ISBI 2018 : Proceedings of the International Symposium on Biomedical Imaging, IEEE, Piscataway, N.J., pp. 766-769, doi: 10.1109/ISBI.2018.8363685.

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Title A time domain classification of steady-state visual evoked potentials using deep recurrent-convolutional neural networks
Author(s) Attia, MohammedORCID iD for Attia, Mohammed orcid.org/0000-0002-4220-0970
Hettiarachchi, ImaliORCID iD for Hettiarachchi, Imali orcid.org/0000-0002-1593-6296
Hossny, MohammedORCID iD for Hossny, Mohammed orcid.org/0000-0002-0360-5270
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name Biomedical Imaging. Symposium (2018 : Washington, D.C.)
Conference location Washington, D.C.
Conference dates 2018/04/04 - 2018/04/07
Title of proceedings ISBI 2018 : Proceedings of the International Symposium on Biomedical Imaging
Publication date 2018
Start page 766
End page 769
Total pages 4
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) EEG
BCI
SSVEP
CNN
RNN
time domain
ISBN 9781538636367
ISSN 1945-7928
1945-8452
Language eng
DOI 10.1109/ISBI.2018.8363685
Field of Research 090302 Biomechanical Engineering
080106 Image Processing
Socio Economic Objective 920502 Health Related to Ageing
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2018, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30109627

Document type: Conference Paper
Collections: Institute for Frontier Materials
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