Kim Jonghwa, André Elisabeth
Institut für Informatik, University of Augsburg, Eichleitnerstr. 30, D-86159, Augsburg, Germany.
IEEE Trans Pattern Anal Mach Intell. 2008 Dec;30(12):2067-83. doi: 10.1109/TPAMI.2008.26.
Little attention has been paid so far to physiological signals for emotion recognition compared to audiovisual emotion channels such as facial expression or speech. This paper investigates the potential of physiological signals as reliable channels for emotion recognition. All essential stages of an automatic recognition system are discussed, from the recording of a physiological dataset to a feature-based multiclass classification. In order to collect a physiological dataset from multiple subjects over many weeks, we used a musical induction method which spontaneously leads subjects to real emotional states, without any deliberate lab setting. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to find the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by classification results. Classification of four musical emotions (positive/high arousal, negative/high arousal, negative/low arousal, positive/low arousal) is performed by using an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we develop a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA. Improved recognition accuracy of 95% and 70% for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme.
与面部表情或语音等视听情感通道相比，到目前为止，生理信号在情感识别方面受到的关注较少。本文研究了生理信号作为情感识别可靠通道的潜力。讨论了自动识别系统的所有关键阶段，从生理数据集的记录到基于特征的多类分类。为了在数周内从多个受试者收集生理数据集，我们使用了一种音乐诱导方法，该方法能使受试者自发进入真实的情感状态，无需任何刻意的实验室环境。使用四通道生物传感器来测量肌电图、心电图、皮肤电导率和呼吸变化。提出了来自各种分析领域的广泛生理特征，包括时间/频率、熵、几何分析、子带谱、多尺度熵等，以便找到与情感最相关的特征并将它们与情感状态相关联。详细说明了提取的最佳特征，并通过分类结果证明了它们的有效性。使用扩展线性判别分析（pLDA）对四种音乐情感（积极/高唤醒、消极/高唤醒、消极/低唤醒、积极/低唤醒）进行分类。此外，通过利用二维情感模型的二分特性，我们开发了一种新颖的情感特定多级二分分类（EMDC）方案，并将其性能与使用pLDA的直接多类分类进行比较。使用EMDC方案分别在受试者依赖和受试者独立分类中实现了95%和70%的更高识别准确率。
