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There are still many challenges of emotion recognition using physiologicaldata despite the substantial progress made recently. In this paper, weattempted to address two major challenges. First, in order to deal with thesparsely-labeled physiological data, we first decomposed the raw physiologicaldata using signal spectrum analysis, based on which we extracted bothcomplexity and energy features. Such a procedure helped reduce noise andimprove feature extraction effectiveness. Second, in order to improve theexplainability of the machine learning models in emotion recognition withphysiological data, we proposed Light Gradient Boosting Machine (LightGBM) andSHapley Additive exPlanations (SHAP) for emotion prediction and modelexplanation, respectively. The LightGBM model outperformed the eXtreme GradientBoosting (XGBoost) model on the public Database for Emotion Analysis usingPhysiological signals (DEAP) with f1-scores of 0.814, 0.823, and 0.860 forbinary classification of valence, arousal, and liking, respectively, withcross-subject validation using eight peripheral physiological signals.Furthermore, the SHAP model was able to identify the most important features inemotion recognition, and revealed the relationships between the predictorvariables and the response variables in terms of their main effects andinteraction effects. Therefore, the results of the proposed model not only hadgood performance using peripheral physiological data, but also gave moreinsights into the underlying mechanisms in recognizing emotions.

Cross-Subject Emotion Recognition with Sparsely-Labeled Peripheral Physiological Data Using SHAP-Explained Tree Ensembles