Faculty of Mechanical and Automotive Engineering, Catholic University of Daegu, Hayang, Gyeongsan-Si, Gyeongbuk 712-702, Republic of Korea.
Comput Biol Med. 2013 Dec;43(12):2230-7. doi: 10.1016/j.compbiomed.2013.10.017. Epub 2013 Oct 26.
This paper addresses the emotion recognition problem from electroencephalogram signals, in which emotions are represented on the valence and arousal dimensions. Fast Fourier transform analysis is used to extract features and the feature selection based on Pearson correlation coefficient is applied. This paper proposes a probabilistic classifier based on Bayes' theorem and a supervised learning using a perceptron convergence algorithm. To verify the proposed methodology, we use an open database. An emotion is defined as two-level class and three-level class in both valence and arousal dimensions. For the two-level class case, the average accuracy of the valence and arousal estimation is 70.9% and 70.1%, respectively. For the three-level class case, the average accuracy is 55.4% and 55.2%, respectively.
本文针对情绪识别问题，从脑电信号中提取情绪的效价和唤醒维度。采用快速傅里叶变换分析提取特征，并基于皮尔逊相关系数进行特征选择。本文提出了一种基于贝叶斯定理的概率分类器和基于感知器收敛算法的监督学习。为了验证所提出的方法，我们使用了一个公开的数据库。在效价和唤醒维度中，情绪被定义为两级类和三级类。对于两级类情况，效价和唤醒估计的平均准确率分别为 70.9%和 70.1%。对于三级类情况，平均准确率分别为 55.4%和 55.2%。
