Abstract
In this study, we propose combining non-linear feature representations, namely Hurst Exponent, correlation dimension, and largest Lyapunov exponent, with TabNet, a novel attention-based neural network architecture, to perform EEG-based decoding of memory formation in single trials. Our results show that these combinations perform favourably when compared to current state-of-the-art approaches based on convolutional neural networks. Moreover, the interpretability of TabNet revealed that its feature selection for decision making is valid from a neurophysiological perspective, which is an advantage compared to other models.
Original language | English |
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Title of host publication | 10th International Winter Conference on Brain-Computer Interface, BCI 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781665413374 |
DOIs | |
Publication status | Published - 2022 |
Event | 10th International Winter Conference on Brain-Computer Interface, BCI 2022 - Gangwon-do, Korea, Republic of Duration: 2022 Feb 21 → 2022 Feb 23 |
Publication series
Name | International Winter Conference on Brain-Computer Interface, BCI |
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Volume | 2022-February |
ISSN (Print) | 2572-7672 |
Conference
Conference | 10th International Winter Conference on Brain-Computer Interface, BCI 2022 |
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Country/Territory | Korea, Republic of |
City | Gangwon-do |
Period | 22/2/21 → 22/2/23 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Signal Processing