Ultra-Lightweight BNN for Real-Time EEG Emotion Recognition on Wearable Devices

This paper proposes an efficient 1D-Binary Neural Network (1D-BNN) designed for real-time emotion recognition based on EEG signals, with a focus on optimization for deployment on wearable devices. The model processes raw EEG signals in an end-to-end manner by utilizing 1D convolution operations together with binary quantization, which enhances temporal feature extraction while significantly reducing computational complexity and memory requirements. Evaluated on the DEAP dataset, the model achieves a high classification accuracy of 97.53% for valence and 98.11% for arousal, and it reaches 98.39% for four-class emotion recognition tasks, all while maintaining an extremely compact model size of only 0.173MB. These experimental results demonstrate that the proposed 1D-BNN is highly suitable for energy-efficient, low-latency deployment in resource-constrained environments, thereby enabling practical and effective integration into wearable emotion recognition systems. Overall, this study highlights the significant potential of binarized neural networks to achieve a well-balanced trade-off between performance and computational efficiency, providing valuable insights for the development of future embedded emotion recognition applications.