SwinTaste: Bimodal Biosignals Taste Sensation Recognition via Swin Transformer

Objective assessment of taste sensation is essential for medical diagnosis, food development, and multisensory interaction. Human taste sensation can be characterized through biosignals such as electroencephalography (EEG) and electromyography (EMG). However, taste sensation recognition on multiple-subject datasets remains challenging due to the low signal-to-noise ratio and substantial individual variability of biosignals. To address these problems, we propose SwinTaste for accurate and generalized taste sensation recognition from bimodal biosignals. The Transformer is introduced to extract hierarchical features. A two-stage patch partition module is optimized for the characteristics of biosignals. Moreover, a multi-task learning strategy is adopted to improve the generalization and subject adaptation abilities. The SwinTaste is evaluated on a multiple-subject taste sensation dataset. Comparison experiments and ablation studies demonstrate the superior performance of SwinTaste, indicating the potential for generalized application in biosignal recognition.