MASA-TCN: Multi-Anchor Space-Aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition

Emotion recognition from electroencephalogram (EEG) signals is a critical domain in biomedical research with applications ranging from mental disorder regulation to human-computer interaction. In this paper, we address two fundamental aspects of EEG emotion recognition: continuous regression of emotional states and discrete classification of emotions. While classification methods have garnered significant attention, regression methods remain relatively under-explored. To bridge this gap, we introduce MASA-TCN, a novel unified model that leverages the spatial learning capabilities of Temporal Convolutional Networks (TCNs) for EEG emotion regression and classification tasks. The key innovation lies in the introduction of a space-aware temporal layer, which empowers TCN to capture spatial relationships among EEG electrodes, enhancing its ability to discern nuanced emotional states. Additionally, we design a multi-anchor block with attentive fusion, enabling the model to adaptively learn dynamic temporal dependencies within the EEG signals. Experiments on two publicly available datasets show that MASA-TCN achieves higher results than the state-of-the-art methods for both EEG emotion regression and classification tasks.