Enhancing P300 Detection in Brain-Computer Interfaces with Interpretable Post-processing of Recurrent Neural Networks

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

doi:10.1007/978-3-031-44223-0_3

Enhancing P300 Detection in Brain-Computer Interfaces with Interpretable Post-processing of Recurrent Neural Networks

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

Abstract

Brain-computer interfaces (BCIs) are innovative systems that allow individuals to communicate with external devices without physical movements. These systems commonly use Event-Related Potentials (ERPs), particularly P300, as the signal control. However, despite their wide acceptance, there are still issues to be resolved, such as inter- and intra-subject variability. To address this challenge, we propose a novel approach based on post-processing the output of a Recurrent Neural Network using a Post-Recurrent Module (PRM). The PRM processes the temporal information extracted from the recurrent layer to make the final decision. This work shows that simple approaches, such as a reduce-max operation or a logistic regression layer, can improve the balanced accuracy by more than 9 % compared to state-of-the-art results. Our findings also contribute to the interpretability of RNNs since we have deepened the internal mechanisms of the model through an extensive analysis of the PRM layer. Overall, this study enhances the performance of ERP-based BCIs.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings
Editors	Lazaros Iliadis, Antonios Papaleonidas, Plamen Angelov, Chrisina Jayne
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	25-36
Number of pages	12
ISBN (Print)	9783031442223
DOIs	https://doi.org/10.1007/978-3-031-44223-0_3
State	Published - 2023
Event	32nd International Conference on Artificial Neural Networks, ICANN 2023 - Heraklion, Greece Duration: 26 Sep 2023 → 29 Sep 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14259 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	32nd International Conference on Artificial Neural Networks, ICANN 2023
Country/Territory	Greece
City	Heraklion
Period	26/09/23 → 29/09/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Bayesian Linear Discriminant Analysis
Brain-Machine Interface
Deep Learning
Elman RNN
ERP Detection
Inter- and Intra-subject Variability
Interpretability
LSTM

Access to Document

10.1007/978-3-031-44223-0_3

Cite this

Oliva, C., Changoluisa, V., Rodríguez, F. B., & Lago-Fernández, L. F. (2023). Enhancing P300 Detection in Brain-Computer Interfaces with Interpretable Post-processing of Recurrent Neural Networks. In L. Iliadis, A. Papaleonidas, P. Angelov, & C. Jayne (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings (pp. 25-36). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14259 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-44223-0_3

Oliva, Christian ; Changoluisa, Vinicio ; Rodríguez, Francisco B. et al. / Enhancing P300 Detection in Brain-Computer Interfaces with Interpretable Post-processing of Recurrent Neural Networks. Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings. editor / Lazaros Iliadis ; Antonios Papaleonidas ; Plamen Angelov ; Chrisina Jayne. Springer Science and Business Media Deutschland GmbH, 2023. pp. 25-36 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Enhancing P300 Detection in Brain-Computer Interfaces with Interpretable Post-processing of Recurrent Neural Networks",

abstract = "Brain-computer interfaces (BCIs) are innovative systems that allow individuals to communicate with external devices without physical movements. These systems commonly use Event-Related Potentials (ERPs), particularly P300, as the signal control. However, despite their wide acceptance, there are still issues to be resolved, such as inter- and intra-subject variability. To address this challenge, we propose a novel approach based on post-processing the output of a Recurrent Neural Network using a Post-Recurrent Module (PRM). The PRM processes the temporal information extracted from the recurrent layer to make the final decision. This work shows that simple approaches, such as a reduce-max operation or a logistic regression layer, can improve the balanced accuracy by more than 9 % compared to state-of-the-art results. Our findings also contribute to the interpretability of RNNs since we have deepened the internal mechanisms of the model through an extensive analysis of the PRM layer. Overall, this study enhances the performance of ERP-based BCIs.",

keywords = "Bayesian Linear Discriminant Analysis, Brain-Machine Interface, Deep Learning, Elman RNN, ERP Detection, Inter- and Intra-subject Variability, Interpretability, LSTM",

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Oliva, C, Changoluisa, V, Rodríguez, FB & Lago-Fernández, LF 2023, Enhancing P300 Detection in Brain-Computer Interfaces with Interpretable Post-processing of Recurrent Neural Networks. in L Iliadis, A Papaleonidas, P Angelov & C Jayne (eds), Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd 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. 14259 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 25-36, 32nd International Conference on Artificial Neural Networks, ICANN 2023, Heraklion, Greece, 26/09/23. https://doi.org/10.1007/978-3-031-44223-0_3

Enhancing P300 Detection in Brain-Computer Interfaces with Interpretable Post-processing of Recurrent Neural Networks. / Oliva, Christian; Changoluisa, Vinicio; Rodríguez, Francisco B. et al.
Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings. ed. / Lazaros Iliadis; Antonios Papaleonidas; Plamen Angelov; Chrisina Jayne. Springer Science and Business Media Deutschland GmbH, 2023. p. 25-36 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14259 LNCS).

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

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AU - Lago-Fernández, Luis F.

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AB - Brain-computer interfaces (BCIs) are innovative systems that allow individuals to communicate with external devices without physical movements. These systems commonly use Event-Related Potentials (ERPs), particularly P300, as the signal control. However, despite their wide acceptance, there are still issues to be resolved, such as inter- and intra-subject variability. To address this challenge, we propose a novel approach based on post-processing the output of a Recurrent Neural Network using a Post-Recurrent Module (PRM). The PRM processes the temporal information extracted from the recurrent layer to make the final decision. This work shows that simple approaches, such as a reduce-max operation or a logistic regression layer, can improve the balanced accuracy by more than 9 % compared to state-of-the-art results. Our findings also contribute to the interpretability of RNNs since we have deepened the internal mechanisms of the model through an extensive analysis of the PRM layer. Overall, this study enhances the performance of ERP-based BCIs.

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Oliva C, Changoluisa V, Rodríguez FB, Lago-Fernández LF. Enhancing P300 Detection in Brain-Computer Interfaces with Interpretable Post-processing of Recurrent Neural Networks. In Iliadis L, Papaleonidas A, Angelov P, Jayne C, editors, Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 25-36. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-44223-0_3