Comparison of Convolutional and Recurrent Neural Networks for the P300 Detection

Lukáš Vařeka

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 Harvard Style

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 - Volume 2: BIOSIGNALS, ISBN 978-989-758-490-9, pages 186-191. DOI: 10.5220/0010248201860191


in Bibtex Style

@conference{biosignals21,
author={Lukáš Vařeka},
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 - Volume 2: BIOSIGNALS,},
year={2021},
pages={186-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010248201860191},
isbn={978-989-758-490-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS,
TI - Comparison of Convolutional and Recurrent Neural Networks for the P300 Detection
SN - 978-989-758-490-9
AU - Vařeka L.
PY - 2021
SP - 186
EP - 191
DO - 10.5220/0010248201860191