Interpretability of Hybrid Feature Using Graph Neural Networks from Mental Arithmetic Based EEG

Min Kyung Jung, Hakseung Kim, Seho Lee, Jung Bin Kim, Dong Joo Kim

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

2 Citations (Scopus)

Abstract

A high cognitive load could significantly impair problem-solving skills. Electroencephalogram (EEG)-based real-time assessment of mental workload is feasible, and graph neural networks (GNN) can classify brain activity patterns during cognitively demanding tasks with high accuracy. However, previous GNN studies pertaining to mental workload classification lack explainability. This study utilized a state-of-the-art GNN variant with GNNexplainer to find relevant connectivity during mental arithmetic (MA) tasks. In this endeavor, MA EEG recordings were retrieved from an openaccess database. The signals were transformed to graph data through the envelope correlation and power spectral density (PSD), and subjected to GNN with hierarchical graph pooling with a structure learning model to classify MA and baseline (BL). The model accuracy was $85.57 \pm 6.27$ and $96.26 \pm 4.14$% for the connectivity dataset and the PSD and the connectivity feature, respectively. Among the connections between nodes identified as important by GNNExplainer, two notable edge patterns were found as 1) from the left centro-parietal region to left frontal regions, and 2) the frontoparietal connection. The results indicate 1) the GNN model performance could be improved using the connectivity and PSD feature together, and 2) characteristic patterns of the connectome and PSD could be important for MA classification. The connectivity analysis by the ''explainable'' GNN model could be beneficial in future brain activity pattern studies.

Original languageEnglish
Title of host publication11th International Winter Conference on Brain-Computer Interface, BCI 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665464444
DOIs
Publication statusPublished - 2023
Event11th International Winter Conference on Brain-Computer Interface, BCI 2023 - Virtual, Online, Korea, Republic of
Duration: 2023 Feb 202023 Feb 22

Publication series

NameInternational Winter Conference on Brain-Computer Interface, BCI
Volume2023-February
ISSN (Print)2572-7672

Conference

Conference11th International Winter Conference on Brain-Computer Interface, BCI 2023
Country/TerritoryKorea, Republic of
CityVirtual, Online
Period23/2/2023/2/22

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • brain computer interface
  • conncetivity
  • electroencephalography
  • explainable artificial intelligence
  • graph neural networks
  • mental arithmetic task

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Signal Processing

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