Spatial Feature Regularization and Label Decoupling Based Cross-Subject Motor Imagery EEG Decoding

Yifan Zhou, Tian jian Luo*, Xiaochen Zhang, Te Han

*Corresponding author for this work

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

Abstract

Motor imagery (MI) serves as a vital approach to constructing brain-computer interfaces (BCIs) based on electroencephalogram (EEG) signals. However, the time-variant and label-coupling characteristics of EEG signals, combined with the limited sample sizes, often necessitate MI-EEG decoding across subjects. Unfortunately, existing methods encounter challenges related to interference from out-of-distribution features and feature-label coupling, resulting in the deterioration of decoding performance. To address these issues, this paper proposes a novel MI-EEG feature learning framework that focuses on decoupling features from labels and regularizing the feature representation. The proposed framework leverages aligned MI-EEG samples to extract Gaussian weighting regularized spatial features. Subsequently, a domain adaptation method is employed to decouple the extracted features from labels across different subjects’ domains, thereby facilitating cross-subject MI-EEG decoding. To evaluate the effectiveness and efficiency of the proposed method, we conducted experiments using three benchmark MI-EEG datasets, consisting of four distinct groups of experiments. The experimental results demonstrate the effectiveness, efficiency, and parameter insensitivity of the proposed method, indicating its significant application value in the field of MI-EEG decoding.

Original languageEnglish
Title of host publicationPattern Recognition and Computer Vision - 6th Chinese Conference, PRCV 2023, Proceedings
EditorsQingshan Liu, Hanzi Wang, Rongrong Ji, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages407-423
Number of pages17
ISBN (Print)9789819985579
DOIs
Publication statusPublished - 2024
Event6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023 - Xiamen, China
Duration: 13 Oct 202315 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14437 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023
Country/TerritoryChina
CityXiamen
Period13/10/2315/10/23

Keywords

  • EEG Decoding
  • Label Decoupling
  • Motor Imagery
  • Pattern Recognition
  • Spatial Feature Regularization

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Zhou, Y., Luo, T. J., Zhang, X., & Han, T. (2024). Spatial Feature Regularization and Label Decoupling Based Cross-Subject Motor Imagery EEG Decoding. In Q. Liu, H. Wang, R. Ji, Z. Ma, W. Zheng, H. Zha, X. Chen, & L. Wang (Eds.), Pattern Recognition and Computer Vision - 6th Chinese Conference, PRCV 2023, Proceedings (pp. 407-423). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14437 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-8558-6_34