Reali Pierluigi, Cosentini Claudia, Carvalho Paulo de, Traver Vicente, Bianchi Anna M
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:110-113. doi: 10.1109/EMBC.2018.8512236.
In the last decades numerous researches have revealed a strong link between emotions and several physiological responses. However, the automatic recognition of emotions still remains a challenge. In this work we describe a novel approach to estimate valence, arousal and dominance values from various biological parameters (derived from electrodermal activity, heart rate variability signal and electroencephalography), by means of multiple linear regression models. The models training was performed by using a set of pictures pre-evaluated in terms of valence, arousal and dominance, selected from the International Affective Picture System (IAPS) database. By using the step-wise regression method, all the possible combinations of considered biological parameters were tested as input variables for the models. The three multiple linear regression models that could provide the best fit for IAPS pictures valence, arousal and dominance values were selected. The features included in the optimal models were the average of the inter-beat duration (mean RR), the EEG spectral power computed in alpha, beta and theta frequency bands (Alpha, Beta and Theta power) and the average value of EDA signal (mean EDA). The obtained models show an overall good performance in predicting valence, arousal and dominance values.
在过去几十年中，众多研究揭示了情绪与多种生理反应之间的紧密联系。然而，情绪的自动识别仍然是一项挑战。在这项工作中，我们描述了一种新颖的方法，通过多元线性回归模型，从各种生物参数（源自皮肤电活动、心率变异性信号和脑电图）中估计效价、唤醒度和优势度值。模型训练是通过使用一组从国际情感图片系统（IAPS）数据库中选取的、在效价、唤醒度和优势度方面经过预先评估的图片来进行的。通过逐步回归方法，将所考虑的生物参数的所有可能组合作为模型的输入变量进行测试。选择了能够为IAPS图片的效价、唤醒度和优势度值提供最佳拟合的三个多元线性回归模型。最优模型中包含的特征有心跳间期的平均值（平均RR）、在α、β和θ频段计算的脑电图频谱功率（α、β和θ功率）以及皮肤电活动信号的平均值（平均EDA）。所获得的模型在预测效价、唤醒度和优势度值方面总体表现良好。
