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Author: Lukáš Vařeka

Affiliation: NTIS - New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Univerzitní 8, 306 14 Plzeň, Czech Republic

Keyword(s): Convolutional Neural Networks, Recurrent Neural Networks, LDA, EEG, ERP, P300.

Abstract: Single-trial classification of the P300 component is a difficult task because of the low signal to noise ratio. However, its application to brain-computer interface development can significantly improve the usability of these systems. This paper presents a comparison of baseline linear discriminant analysis (LDA) with convolutional (CNN) and recurrent neural networks (RNN) for the P300 classification. The experiments were based on a large multi-subject publicly available dataset of school-age children. Several hyperparameter choices were experimentally investigated and discussed. The presented CNN slightly outperformed both RNN and baseline LDA classifier (the accuracy of 63.2 % vs. 61.3 % and 62.8 %). The differences were most pronounced in precision and recall. Implications of the results and proposals for future work, e.g., stacked CNN–LSTM, are discussed.

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Paper citation in several formats:
Vařeka and L. (2021). Comparison of Convolutional and Recurrent Neural Networks for the P300 Detection. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - BIOSIGNALS; ISBN 978-989-758-490-9; ISSN 2184-4305, SciTePress, pages 186-191. DOI: 10.5220/0010248200002865

@conference{biosignals21,
author={Lukáš Va\v{r}eka},
title={Comparison of Convolutional and Recurrent Neural Networks for the P300 Detection},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - BIOSIGNALS},
year={2021},
pages={186-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010248200002865},
isbn={978-989-758-490-9},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - BIOSIGNALS
TI - Comparison of Convolutional and Recurrent Neural Networks for the P300 Detection
SN - 978-989-758-490-9
IS - 2184-4305
AU - Vařeka, L.
PY - 2021
SP - 186
EP - 191
DO - 10.5220/0010248200002865
PB - SciTePress

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