Multi-Scale Attention-Based Dense Spatial-Temporal Model for Emotion Induction in Response to Olfactory Stimuli

Affective Brain-Computer Interfaces (aBCIs) have attracted growing attention due to their potential for decoding human emotional states through electroencephalogram (EEG) signals. However, existing deep learning models often struggle to fully capture both the spatial and temporal dependencies in EEG data, resulting in suboptimal performance in emotion classification. To address these challenges, we propose a novel Multi-Scale Attention-Based Dense Spatial-Temporal Model (MSADM). This model leverages temporal attention, multi-scale dense feature extraction, and attention-based feature fusion to effectively capture and enhance the complicated spatial-temporal dependencies within EEG data, thereby improving the representations. Furthermore, we introduce a new olfactory-based emotion induction paradigm, which effectively mitigates the limitations of traditional visual and auditory stimuli by providing more stable and sustained emotional responses. We also present a novel EEG dataset involving 32 subjects, developed through a pilot study to select appropriate olfactory stimuli. Experimental results demonstrate that our model significantly outperforms existing methods across multiple metrics, demonstrating the effectiveness of both the proposed model and emotion induction paradigm. This study underscores the potential of olfactory stimuli and advanced spatial-temporal modeling techniques for enhancing the robustness and performance of emotion recognition.