Multi-Scale Masked Autoencoders for Cross-Session Emotion Recognition

Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary char...

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Vydáno v:IEEE transactions on neural systems and rehabilitation engineering Ročník 32; s. 1637 - 1646
Hlavní autoři: Pang, Miaoqi, Wang, Hongtao, Huang, Jiayang, Vong, Chi-Man, Zeng, Zhiqiang, Chen, Chuangquan
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adult
Algorithms
Brain modeling
Brain-Computer Interfaces
cross-session
Data collection
Data mining
Data models
EEG
EEG-based emotion recognition
Electroencephalography
Electroencephalography - methods
Emotion recognition
Emotional factors
Emotions
Emotions - physiology
Feature extraction
Female
Human-computer interface
Humans
Invariants
Machine Learning
Male
Neural Networks, Computer
Representations
Robustness
self-supervised learning
Task analysis
transformer
ISSN:1534-4320, 1558-0210, 1558-0210
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Shrnutí:Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary characteristics of EEG data, and noise artifacts in EEG data collection pose formidable challenges in developing subject-specific cross-session emotion recognition models. To simultaneously address these challenges, we propose a unified pre-training framework based on multi-scale masked autoencoders (MSMAE), which utilizes large-scale unlabeled EEG signals from multiple subjects and sessions to extract noise-robust, subject-invariant, and temporal-invariant features. We subsequently fine-tune the obtained generalized features with only a small amount of labeled data from a specific subject for personalization and enable cross-session emotion recognition. Our framework emphasizes: 1) multi-scale representation to capture diverse aspects of EEG signals, obtaining comprehensive information; 2) an improved masking mechanism for robust channel-level representation learning, addressing missing channel issues while preserving inter-channel relationships; and 3) invariance learning for regional correlations in spatial-level representation, minimizing inter-subject and inter-session variances. Under these elaborate designs, the proposed MSMAE exhibits a remarkable ability to decode emotional states from a different session of EEG data during the testing phase. Extensive experiments conducted on the two publicly available datasets, i.e., SEED and SEED-IV, demonstrate that the proposed MSMAE consistently achieves stable results and outperforms competitive baseline methods in cross-session emotion recognition.
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ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2024.3389037