Comparison of convolutional and recurrent neural networks for the P300 detection

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of convolutional and recurrent neural networks for the P300 detection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Comparison of convolutional and recurrent neural networks for the P300 detection

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - (Volume 4)

ISBN

978-989-758-490-9

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

186-191

Název nakladatele

SCITEPRESS – Science and Technology Publications, Lda

Místo vydání

Setúbal

Místo konání akce

online

Datum konání akce

11. 2. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000664110100020