There are still many challenges of emotion recognition using physiological data despite the substantial progress made recently. In this paper, we attempted to address two major challenges. First, in order to deal with the sparsely-labeled physiological data, we first decomposed the raw physiological data using signal spectrum analysis, based on which we extracted both complexity and energy features. Such a procedure helped reduce noise and improve feature extraction effectiveness. Second, in order to improve the explainability of the machine learning models in emotion recognition with physiological data, we proposed Light Gradient Boosting Machine (LightGBM) and SHapley Additive exPlanations (SHAP) for emotion prediction and model explanation, respectively. The LightGBM model outperformed the eXtreme Gradient Boosting (XGBoost) model on the public Database for Emotion Analysis using Physiological signals (DEAP) with f1-scores of 0.814, 0.823, and 0.860 for binary classification of valence, arousal, and liking, respectively, with cross-subject validation using eight peripheral physiological signals. Furthermore, the SHAP model was able to identify the most important features in emotion recognition, and revealed the relationships between the predictor variables and the response variables in terms of their main effects and interaction effects. Therefore, the results of the proposed model not only had good performance using peripheral physiological data, but also gave more insights into the underlying mechanisms in recognizing emotions.
翻译:尽管最近取得了巨大进展,但利用生理数据来认识情感,这方面仍然存在许多挑战。在本文件中,我们试图应对两大挑战。首先,为了处理标记不多的生理数据,我们首先使用信号频谱分析对原始生理数据进行分解,我们根据这种分析提取了复杂性和能量特征。这种程序有助于减少噪音,提高特征提取效力。第二,为了改进机器学习模型在情感识别中与生理数据相比的情感识别的可解释性,我们提议轻度增压机器(LightGBM)和Shanapley Additive Explations (SHAP)分别用于情感预测和模型解释。首先,为了处理标记不多的生理数据数据数据,我们首先利用光度GHBM模型模型(XGBM)模型(XGBoost)模型(XGBO)利用信号分析复杂性和能量特性分析了情感分析公共数据库(DEAP)的原始信号(F1核心信号为0.814、0.823和0.860),我们提议对价值、振奋度和感知度的二元分类分别进行交叉校准验证,并使用八个周边生理信号信号信号信号信号信号信号信号信号信号信号。此外,SHAP模型模型模型模型(SHAP模型)模型模型模型模型模型(SHIS变的模型)模型(HAFRI反应)模型(SV)模型(SVIF)模型)模型(S)模型(SV)模型(S)模型)模型(SV)模型(SV)模型(SV)模型(SV)的功能(SDFIFIFIFIFL)中,也得以在预测和S)中揭示了它们(S)中显示了它们的主要变量(SVD)中,在预测和动作(SVD)中显示了它们的主要变量(SD)中,在预测和动作(SVDF)中,在预测和最重要的功能反应反应)中,也只能变数变数中,在预测和最重要关系(SVDFIFVDFDFLVD)中,也只是中,在确认了。