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A time domain classification of steady-state visual evoked potentials using deep recurrent-convolutional neural networks
conference contribution
posted on 2018-05-23, 00:00 authored by Mohamed Hassan Attia, Imali HettiarachchiImali Hettiarachchi, Mohammed Hossny, Saeid Nahavandi© 2018 IEEE. Steady-State Visual Evoked Potential (SSVEP) is one of the popular methods of brain-computer interfacing (BCI). It is used to translate the Electroencephalogram (EEG) signals into actions or choices. The main challenge in processing the SSVEP signal recognition is finding an appropriate intermediate representation to facilitate the classification task afterwards. In the literature, frequency domain analysis was extensively adopted as an intermediate representation for SSVEP classification. In this presented paper, we propose a deep learning model that uses a hybrid architecture based on Convolutional and Recurrent Neural Networks to classify SSVEP signals in the time domain directly. We achieved accuracy 93.59% compared to 87.40% for the state-of-the-art method: canonical correlation analysis in the frequency domain. The proposed architecture facilitates the real-time classification of SSVEP signals in the time domain for real-time applications such as robot cars and exoskeletons.
History
Event
Biomedical Imaging. Symposium (2018 : Washington, D.C.)Pagination
766 - 769Publisher
IEEELocation
Washington, D.C.Place of publication
Piscataway, N.J.Publisher DOI
Start date
2018-04-04End date
2018-04-07ISSN
1945-7928eISSN
1945-8452ISBN-13
9781538636367Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2018, IEEETitle of proceedings
ISBI 2018 : Proceedings of the International Symposium on Biomedical ImagingUsage metrics
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