EEGMatch : Learning with Incomplete Labels for Semisupervised EEG-Based Cross-Subject Emotion Recognition
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
---|---|
Journal / Publication | IEEE Transactions on Neural Networks and Learning Systems |
Publication status | Published - 18 Nov 2024 |
Link(s)
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)
EEGMatch: Learning with Incomplete Labels for Semisupervised EEG-Based Cross-Subject Emotion Recognition. / Zhou, Rushuang; Ye, Weishan; Zhang, Zhiguo et al.
In: IEEE Transactions on Neural Networks and Learning Systems, 18.11.2024.
In: IEEE Transactions on Neural Networks and Learning Systems, 18.11.2024.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review