Goshvarpour Atefeh, Goshvarpour Ateke
Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
Department of Biomedical Engineering, Imam Reza International University, Mashhad, Razavi Khorasan, Iran.
Australas Phys Eng Sci Med. 2019 Nov 27. doi: 10.1007/s13246-019-00825-7.
Recently, developing an accurate automatic emotion recognition system using a minimum number of bio-signals has become a challenging issue in "affective computing." This study aimed to propose a reliable system by examining nonlinear dynamics of photoplethysmogram (PPG) and galvanic skin response (GSR). To address this goal, two strategies were adopted. First, the efficiency of each signal in valence/arousal based emotion categorization was examined. Then, the proficiency of a hybrid feature, by combining both GSR and PPG features was studied. Lyapunov exponents, lagged Poincare's measures, and approximate entropy were extracted to characterize the irregularity and chaotic behavior of the phase space. To discriminate two levels of arousal and two levels of the valence, a probabilistic neural network (PNN) with different sigma adjustment parameter was examined. The results showed that the phase space geometry and consequently, the signal dynamics are influenced by the emotional music video. Additionally, distinctive patterns of the phase space behavior were observed under the influence of different lags. For both signals, the most irregularity was observed during the high valence, and the least irregularity was seen during the low valence. Consequently, signals' irregularity is affected by the valence dimension. The results showed that the fusion has more potential for emotion recognition than that of using each signal separately. For sigma = 0.1, the highest recognition rate was 100% in a subject-dependent mode. In a subject-independent mode, the maximum accuracies of 88.57 and 86.8% were obtained for arousal and valence dimensions, respectively.
最近，利用最少数量的生物信号开发精确的自动情感识别系统已成为“情感计算”领域中一个具有挑战性的问题。本研究旨在通过研究光电容积脉搏波描记图（PPG）和皮肤电反应（GSR）的非线性动力学来提出一个可靠的系统。为实现这一目标，采用了两种策略。首先，研究了每个信号在基于效价/唤醒度的情感分类中的效率。然后，研究了结合GSR和PPG特征的混合特征的效能。提取李雅普诺夫指数、滞后庞加莱测度和近似熵来表征相空间的不规则性和混沌行为。为了区分两个唤醒水平和两个效价水平，研究了具有不同西格玛调整参数的概率神经网络（PNN）。结果表明，相空间几何结构以及信号动力学受情感音乐视频的影响。此外，在不同滞后的影响下观察到了相空间行为的独特模式。对于这两种信号，在高唤醒度时观察到的不规则性最大，在低唤醒度时观察到的不规则性最小。因此，信号的不规则性受效价维度的影响。结果表明，与单独使用每个信号相比，融合在情感识别方面具有更大的潜力。对于西格玛 = 0.1，在受试者依赖模式下最高识别率为100%。在受试者独立模式下，唤醒度和效价维度的最大准确率分别为88.57%和86.8%。
