Emotion Recognition Method Based on EEG Signal Reinforcement Learning and CNN

Emotion, as a unique attribute of human beings, is an important research direction of human-computer interaction by using the developed artificial intelligence technology. Although the existing research has reached a high accuracy, the efficiency problem has not been completely solved. Aiming at the time-consuming and labor-consuming problem of hyperparameter optimization in traditional deep learning, this paper proposes an automatic parameter adjustment method combining reinforcement learning (RL) and revolutionary neural network (CNN). Based on the DEAP data set, a lightweight 2d-cnn model is constructed by using the temporal and spatial characteristics of EEG signals, and the super parameters such as dropout probability and batch size are dynamically optimized by using the proximal strategy optimization (PPO) and deep deterministic strategy gradient (DDPG) algorithms, respectively. Experiments show that the reinforcement learning driven parameter adjustment method significantly improves the performance of the model: PPO and DDPG respectively improve the accuracy of CNN from 69% to 71%, and DDPG achieves the optimal results in fewer training rounds. In addition, the parameter trajectory analysis shows that agents tend to increase the batch size and reduce the dropout probability to balance the risk of feature retention and overfitting. This study verifies the potential of reinforcement learning in hyperparameter optimization, and provides a new idea for automated optimization of complex models. Future work will be extended to higher dimensional parameter space and multimodal data fusion scenarios.