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.
|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|
|Number of pages||12|
|State||Published - 2021|
|Event||30th International Conference on Artificial Neural Networks, ICANN 2021 - Virtual, Online|
Duration: 14 Sep 2021 → 17 Sep 2021
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||30th International Conference on Artificial Neural Networks, ICANN 2021|
|Period||14/09/21 → 17/09/21|
Bibliographical noteFunding 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).
© 2021, Springer Nature Switzerland AG.
- Bayesian LDA
- Detection of P300 at sample level
- Event-related potential
- Inter- and intra-subject variability
- Oddball paradigm
- P300 latency variability
- Recurrent neural networks