Comparison of convolutional and recurrent neural networks for the P300 detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43961411" target="_blank" >RIV/49777513:23520/21:43961411 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.5220/0010248201860191" target="_blank" >https://doi.org/10.5220/0010248201860191</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0010248201860191" target="_blank" >10.5220/0010248201860191</a>
Alternative languages
Result language
angličtina
Original language name
Comparison of convolutional and recurrent neural networks for the P300 detection
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - (Volume 4)
ISBN
978-989-758-490-9
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
186-191
Publisher name
SCITEPRESS – Science and Technology Publications, Lda
Place of publication
Setúbal
Event location
online
Event date
Feb 11, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000664110100020