Fig. 4: Emotion decoding framework and performance evaluation.

a Frequency domain characteristics of carotid pulse signals across three emotional states (Neutral, Relieved, and Frustrated), showing distinct amplitude patterns. b Emotion classification workflow: preprocessing pipeline (left) involving DC removal, Z-score normalization, and discrete Fourier transform (DFT), feeding into a classifier based on a 1DCNN architecture (right) for emotion decoding. c Comparison of classification accuracies across machine learning algorithms (SVM, LDA, RF, MLP, and 1DCNN) with and without DFT preprocessing, highlighting improved performance with DFT. d Confusion matrix for emotion classification. e Frequency and magnitude range of different vibrational signal sources (voice, silent speech, breath, carotid pulse) at neck area. f Time-frequency spectrogram of pulse signals with and without strain isolation treatment when vowel “a” both introduced at 2.5 s, demonstrating successful mitigation of speech crosstalk interference after applying the isolation technique.