Explainable machine learning for memory-related decoding via TabNet and non-linear features

Maxim Mametkulov, Abay Artykbayev, Darina Koishigarina, Amina Kenessova, Kamilla Razikhova, Taeho Kang, Christian Wallraven, Siamac Fazli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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 languageEnglish
Title of host publication10th International Winter Conference on Brain-Computer Interface, BCI 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665413374
DOIs
Publication statusPublished - 2022
Event10th International Winter Conference on Brain-Computer Interface, BCI 2022 - Gangwon-do, Korea, Republic of
Duration: 2022 Feb 212022 Feb 23

Publication series

NameInternational Winter Conference on Brain-Computer Interface, BCI
Volume2022-February
ISSN (Print)2572-7672

Conference

Conference10th International Winter Conference on Brain-Computer Interface, BCI 2022
Country/TerritoryKorea, Republic of
CityGangwon-do
Period22/2/2122/2/23

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Signal Processing

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