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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, 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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