MEG Channel Selection Using Copula Entropy-Based Transfer Entropy for Motor Imagery BCI

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Multi-channel magnetoencephalography (MEG) data provides high spatiotemporal resolution for motor imagery (MI)-based brain-machine interfaces (BCIs). However, not all channels contribute to the performance of BCIs. Taking into account the importance of specific channels in measuring their causal relationships with other channels during MI tasks, a novel channel selection method using copula entropy-based transfer entropy (CTE) is proposed to select task-relevant channels. Experiments on a publicly available dataset validate the effectiveness of the proposed methods. Compared to using all channels, channel selection based on CTE can significantly (p < 0.05) improve single-session classification accuracy and greatly reduce the number of MEG channels. Cross-session classification also outperforms the competing method.

Original languageEnglish
Title of host publication46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350371499
DOIs
StatePublished - 2024
Event46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Orlando, United States
Duration: 15 Jul 202419 Jul 2024

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024
Country/TerritoryUnited States
CityOrlando
Period15/07/2419/07/24

Keywords

  • brain-computer interface (BCI)
  • channel selection
  • copula entropy-based transfer entropy (CTE)
  • Magnetoencephalography (MEG)
  • motor imagery (MI)

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