Precise Temporal P300 Detection in Brain Computer Interface EEG Signals Using a Long-Short Term Memory

Christian Oliva; Vinicio Changoluisa; Francisco B. Rodríguez; Luis F. Lago-Fernández

doi:10.1007/978-3-030-86380-7_37

Precise Temporal P300 Detection in Brain Computer Interface EEG Signals Using a Long-Short Term Memory

Christian Oliva, Vinicio Changoluisa, Francisco B. Rodríguez, Luis F. Lago-Fernández

Electronics and Telematics Research Group (GIETEC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Event-Related Potentials (ERP) detection is a latent problem in the clinical, neuroscience, and engineering fields. It is an open challenge that contributes to achieving more accurate and adaptable Brain-Computer Interfaces (BCI). The state-of-the-art typically uses simple classifiers based on Discriminant Analysis due to their little computational demand. Some more recent approaches have started using Deep Learning techniques, but these do not provide any temporal information and rarely focus on detecting the P300 at sample level in electroencephalography (EEG) signals, which would improve the Information Transfer Rate in BCIs. In other research areas, recurrent neural networks have shown high performance in those tasks that require online responses. We propose a new methodology, based on Long-Short Term Memory networks, in a sample level forecast to predict the P300 signal continuously. We get a slight improvement concerning the standard procedure, typically Bayesian Linear Discriminant Analysis, and we also show that the model predicts the occurrence of the P300 ERP at sample level in EEG signals. This brings us the possibility of evaluating the inherent variation between subjects. Our approach contributes to more agile and adaptable BCIs development, going further in the real-life usage of BCIs.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings
Editors	Igor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	457-468
Number of pages	12
ISBN (Print)	9783030863791
DOIs	https://doi.org/10.1007/978-3-030-86380-7_37
State	Published - 2021
Event	30th International Conference on Artificial Neural Networks, ICANN 2021 - Virtual, Online Duration: 14 Sep 2021 → 17 Sep 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12894 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	30th International Conference on Artificial Neural Networks, ICANN 2021
City	Virtual, Online
Period	14/09/21 → 17/09/21

Bibliographical note

Funding Information:
This work has been partially funded by grant S2017/BMD-3688 from Comunidad de Madrid, by Spanish projects MINECO/FEDER TIN2017-84452-R and PID2020-114867RB-I00 (http://www.mineco.gob.es/) and by Predoctoral Research Grants 2015-AR2Q9086 of the Government of Ecuador through the Secretar?a de Educaci?n Superior, Ciencia, Tecnolog?a e Innovaci?n (SENESCYT).

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Bayesian LDA
Detection of P300 at sample level
Event-related potential
Inter- and intra-subject variability
Oddball paradigm
P300 latency variability
Recurrent neural networks

Access to Document

10.1007/978-3-030-86380-7_37

Cite this

Oliva, C., Changoluisa, V., Rodríguez, F. B., & Lago-Fernández, L. F. (2021). Precise Temporal P300 Detection in Brain Computer Interface EEG Signals Using a Long-Short Term Memory. In I. Farkaš, P. Masulli, S. Otte, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings (pp. 457-468). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12894 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-86380-7_37

Oliva, Christian ; Changoluisa, Vinicio ; Rodríguez, Francisco B. et al. / Precise Temporal P300 Detection in Brain Computer Interface EEG Signals Using a Long-Short Term Memory. Artificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings. editor / Igor Farkaš ; Paolo Masulli ; Sebastian Otte ; Stefan Wermter. Springer Science and Business Media Deutschland GmbH, 2021. pp. 457-468 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Event-Related Potentials (ERP) detection is a latent problem in the clinical, neuroscience, and engineering fields. It is an open challenge that contributes to achieving more accurate and adaptable Brain-Computer Interfaces (BCI). The state-of-the-art typically uses simple classifiers based on Discriminant Analysis due to their little computational demand. Some more recent approaches have started using Deep Learning techniques, but these do not provide any temporal information and rarely focus on detecting the P300 at sample level in electroencephalography (EEG) signals, which would improve the Information Transfer Rate in BCIs. In other research areas, recurrent neural networks have shown high performance in those tasks that require online responses. We propose a new methodology, based on Long-Short Term Memory networks, in a sample level forecast to predict the P300 signal continuously. We get a slight improvement concerning the standard procedure, typically Bayesian Linear Discriminant Analysis, and we also show that the model predicts the occurrence of the P300 ERP at sample level in EEG signals. This brings us the possibility of evaluating the inherent variation between subjects. Our approach contributes to more agile and adaptable BCIs development, going further in the real-life usage of BCIs.",

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Oliva, C, Changoluisa, V, Rodríguez, FB & Lago-Fernández, LF 2021, Precise Temporal P300 Detection in Brain Computer Interface EEG Signals Using a Long-Short Term Memory. in I Farkaš, P Masulli, S Otte & S Wermter (eds), Artificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12894 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 457-468, 30th International Conference on Artificial Neural Networks, ICANN 2021, Virtual, Online, 14/09/21. https://doi.org/10.1007/978-3-030-86380-7_37

Precise Temporal P300 Detection in Brain Computer Interface EEG Signals Using a Long-Short Term Memory. / Oliva, Christian; Changoluisa, Vinicio; Rodríguez, Francisco B. et al.
Artificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings. ed. / Igor Farkaš; Paolo Masulli; Sebastian Otte; Stefan Wermter. Springer Science and Business Media Deutschland GmbH, 2021. p. 457-468 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12894 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Oliva C, Changoluisa V, Rodríguez FB, Lago-Fernández LF. Precise Temporal P300 Detection in Brain Computer Interface EEG Signals Using a Long-Short Term Memory. In Farkaš I, Masulli P, Otte S, Wermter S, editors, Artificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 457-468. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-86380-7_37