EEG-Based Emotion Recognition Using Spatial-Temporal Connectivity

The connectivity properties between EEG signaling channels were found to play an important role in emotion recognition. During the generation and change of emotions, the connectivity between EEG signal channels is not only manifested in the spatial domain at a specific time, but also interconnected between different time intervals, which is often overlooked. A novel approach is proposed to exploit the shapes of spatial-temporal connectivity for EEG-based emotion recognition. By quantifying the connectivity strength of EEG signal channels within and across time intervals, spatial-temporal connectivity features are extracted, and these features can be represented by shapes in the three-dimensional space of EEG signals. Through these shapes, a mapping of different emotional states and brain activity is constructed. Subsequently, a parallel multi-scale convolutional neural network is employed to extract discriminative features from these connectivity shapes, facilitating the classification and identification of emotional states. Experimental results on the DEAP dataset show that our method achieves excellent performance, with an average classification accuracy of 93.25% and 93.16% for the two emotion dimensions of valence and arousal, respectively.