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Human taste sensation can be qualitatively described with surfaceelectromyography. However, the pattern recognition models trained on onesubject (the source domain) do not generalize well on other subjects (thetarget domain). To improve the generalizability and transferability of tastesensation models developed with sEMG data, two methods were innovativelyapplied in this study: domain regularized component analysis (DRCA) andconformal prediction with shrunken centroids (CPSC). The effectiveness of thesetwo methods was investigated independently in an unlabeled data augmentationprocess with the unlabeled data from the target domain, and the same cross-useradaptation pipeline were conducted on six subjects. The results show that DRCAimproved the classification accuracy on six subjects (p < 0.05), compared withthe baseline models trained only with the source domain data;, while CPSC didnot guarantee the accuracy improvement. Furthermore, the combination of DRCAand CPSC presented statistically significant improvement (p < 0.05) inclassification accuracy on six subjects. The proposed strategy combining DRCAand CPSC showed its effectiveness in addressing the cross-user datadistribution drift in sEMG-based taste sensation recognition application. Italso shows the potential in more cross-user adaptation applications.

Unsupervised cross-user adaptation in taste sensation recognition based  on surface electromyography with conformal prediction and domain regularized  component analysis