Contrastive reinforced transfer learning for EEG-based emotion recognition with consideration of individual differences

Electroencephalography (EEG) data exhibit significant individual differences, posing challenges in generalizing emotion recognition models across individuals. Transfer learning (TL) can leverage knowledge obtained from other individuals to help models better account for these individual differences. Existing research predominantly focuses on minimizing negative transfer, while this paper aims to mitigate negative transfer and maximize positive transfer. A fundamental deep transfer learning system was examined to achieve maximum positive transfer. An optimal strategy was formulated to choose the most adaptable knowledge, specifically EEG features, from the source domain to accommodate target individual differences. To achieve this optimal strategy, a reinforcement learning algorithm, specifically the Q-learning algorithm, was applied to extract the most beneficial knowledge from the source domain by the Control Agent (CA). Meanwhile, to more accurately assess the effectiveness of actions in reinforcement learning, a direct comparison between the Reinforced Transfer Learning (RTL) and TL methods is conducted during the learning process, and the Contrastive Reinforced Transfer Learning (CRTL) method is finally proposed. The proposed CRTL model achieved average recognition accuracies of 91.26% (valence) and 90.43% (arousal) on the Database for Emotion Analysis using Physiological Signals (DEAP) dataset and 93.57% on the SJTU Emotion EEG Dataset (SEED) dataset, demonstrating its exceptional performance in emotion recognition tasks. Extensive experimental results have demonstrated that the CRTL method shows significant improvements compared to TL by effectively extracting the most beneficial knowledge from the source domain.