Frantzidis Christos A, Bratsas Charalampos, Papadelis Christos L, Konstantinidis Evdokimos, Pappas Costas, Bamidis Panagiotis D
Laboratory of Medical Informatics, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.
IEEE Trans Inf Technol Biomed. 2010 May;14(3):589-97. doi: 10.1109/TITB.2010.2041553. Epub 2010 Feb 17.
This paper proposes a methodology for the robust classification of neurophysiological data into four emotional states collected during passive viewing of emotional evocative pictures selected from the International Affective Picture System. The proposed classification model is formed according to the current neuroscience trends, since it adopts the independency of two emotional dimensions, namely arousal and valence, as dictated by the bidirectional emotion theory, whereas it is gender-specific. A two-step classification procedure is proposed for the discrimination of emotional states between EEG signals evoked by pleasant and unpleasant stimuli, which also vary in their arousal/intensity levels. The first classification level involves the arousal discrimination. The valence discrimination is then performed. The Mahalanobis (MD) distance-based classifier and support vector machines (SVMs) were used for the discrimination of emotions. The achieved overall classification rates were 79.5% and 81.3% for the MD and SVM, respectively, significantly higher than in previous studies. The robust classification of objective emotional measures is the first step toward numerous applications within the sphere of human-computer interaction.
本文提出了一种方法，用于将神经生理数据稳健地分类为在被动观看从国际情感图片系统中选出的情感唤起图片期间收集的四种情绪状态。所提出的分类模型是根据当前神经科学趋势形成的，因为它采用了双向情感理论所规定的两个情感维度（即唤醒度和效价）的独立性，而且它是针对特定性别的。针对区分由愉悦和不愉快刺激诱发的脑电图（EEG）信号中的情绪状态，提出了一种两步分类程序，这些刺激在唤醒/强度水平上也有所不同。第一个分类级别涉及唤醒度辨别。然后进行效价辨别。基于马氏（MD）距离的分类器和支持向量机（SVM）被用于情绪辨别。MD和SVM的总体分类率分别达到了79.5%和81.3%，显著高于先前的研究。客观情绪测量的稳健分类是迈向人机交互领域众多应用的第一步。
