EEGMatch : Learning with Incomplete Labels for Semisupervised EEG-Based Cross-Subject Emotion Recognition

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

  • Weishan Ye
  • Zhiguo Zhang
  • Yanyang Luo
  • Li Zhang
  • Linling Li
  • Gan Huang
  • Zhen Liang

Detail(s)

Original languageEnglish
Journal / PublicationIEEE Transactions on Neural Networks and Learning Systems
Publication statusPublished - 18 Nov 2024

Abstract

Electroencephalography (EEG) is an objective tool for emotion recognition and shows promising performance. However, the label scarcity problem is a main challenge in this field, which limits the wide application of EEG-based emotion recognition. In this article, we propose a novel semisupervised transfer learning framework (EEGMatch) to leverage both labeled and unlabeled EEG data. First, an EEG-Mixup-based data augmentation method is developed to generate more valid samples for model learning. Second, a semisupervised two-step pairwise learning method is proposed to bridge prototypewise and instancewise pairwise learning, where the prototypewise pairwise learning measures the global relationship between EEG data and the prototypical representation of each emotion class and the instancewise pairwise learning captures the local intrinsic relationship among EEG data. Third, a semisupervised multidomain adaptation is introduced to align the data representation among multiple domains (labeled source domain, unlabeled source domain, and target domain), where the distribution mismatch is alleviated. Extensive experiments are conducted on three benchmark databases (SEED, SEED-IV, and SEED-V) under a cross-subject leave-one-subject-out cross-validation evaluation protocol. The results show the proposed EEGMatch performs better than the state-of-the-art methods under different incomplete label conditions (with 5.89% improvement on SEED, 0.93% improvement on SEED-IV, and 0.28% improvement on SEED-V), which demonstrates the effectiveness of the proposed EEGMatch in dealing with the label scarcity problem in emotion recognition using EEG signals. The source code is available at https://github.com/KAZABANA/EEGMatch. © 2024 IEEE.

Research Area(s)

  • Electroencephalography (EEG), emotion recognition, multidomain adaptation, semisupervised learning, transfer learning

Citation Format(s)