TFSNet: EEG-based Emotion Recognition using Temporal and Frequency-Spatial Feature

Electroencephalography (EEG)-based Automatic Emotion Recognition (AER) has gained increasing attention as a reliable tool for affective computing. While prior studies have explored various temporal, frequency, and spatial-domain representations of EEG signals, few have effectively integrated these domains within a unified framework. In this paper, we propose TFSNet, a multi-domain deep learning model that combines temporal, frequency, and spatial features for robust emotion recognition. TFSNet consists of a dual-encoder architecture: a temporal encoder based on state-space modeling (S4D), and a frequency-spatial encoder that leverages CNNs, attention mechanisms, and graph filtering using a physiologically-informed adjacency matrix. These domain-specific embeddings are fused and passed through a classifier for final prediction. Experimental results on the DREAMER dataset demonstrate that TFSNet achieves superior performance across Valence, Arousal, and Dominance emotions, outperforming state-of-the-art models. The results highlight the effectiveness of combining domain-aware representations and spatial connectivity priors for EEG-based emotion recognition and its potential for real-time applications.