Unsupervised Time-Aware Sampling Network With Deep Reinforcement Learning for EEG-Based Emotion Recognition

• Published in: IEEE transactions on affective computing Vol. 15; no. 3; pp. 1090 - 1103
• Main Authors: Zhang, Yongtao, Pan, Yue, Zhang, Yulin, Zhang, Min, Li, Linling, Zhang, Li, Huang, Gan, Su, Lei, Liu, Honghai, Liang, Zhen, Zhang, Zhiguo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: affective brain-computer interface; Brain modeling; Clustering; Convolutional neural networks; Deep learning; deep reinforcement learning; Electroencephalography; Emotion recognition; Emotional factors; Emotions; Feature extraction; Fragments; Human-computer interface; Labels; Markov processes; Reinforcement learning; Sampling; Time series; Unsupervised learning
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2023.3319397

Recognizing human emotions from complex, multivariate, and non-stationary electroencephalography (EEG) time series is essential in affective brain-computer interface. However, because continuous labeling of ever-changing emotional states is not feasible in practice, existing methods can only assign a fixed label to all EEG timepoints in a continuous emotion-evoking trial, which overlooks the highly dynamic emotional states and highly non-stationary EEG signals. To solve the problems of high reliance on fixed labels and ignorance of time-changing information, in this paper we propose a time-aware sampling network ( TAS-Net ) using deep reinforcement learning (DRL) for unsupervised emotion recognition, which is able to detect key emotion fragments and disregard irrelevant and misleading parts. Specifically, we formulate the process of mining key emotion fragments from EEG time series as a Markov decision process and train a time-aware agent through DRL without label information. First, the time-aware agent takes deep features from a feature extractor as input and generates sample-wise importance scores reflecting the emotion-related information each sample contains. Then, based on the obtained sample-wise importance scores, our method preserves top- X continuous EEG fragments with relevant emotion and discards the rest. Finally, we treat these continuous fragments as key emotion fragments and feed them into a hypergraph decoding model for unsupervised clustering. Extensive experiments are conducted on three public datasets (SEED, DEAP, and MAHNOB-HCI) for emotion recognition using leave-one-subject-out cross-validation, and the results demonstrate the superiority of the proposed method against previous unsupervised emotion recognition methods. The proposed TAS-Net has great potential in achieving a more practical and accurate affective brain-computer interface in a dynamic and label-free circumstance.