Hatamikia Sepideh, Maghooli Keivan, Nasrabadi Ali Motie
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Faculty of Engineering, Shahed University, Tehran, Iran.
J Med Signals Sens. 2014 Jul;4(3):194-201.
Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K-nearest neighbor (KNN) classifier using EEG signals during emotional audio-visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg's method) based on Levinson-Durbin's recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies-Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10-15% as compared to Davies-Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively.
脑电图（EEG）是区分不同脑部疾病和精神状态的有用生物信号之一。近年来，从生物信号中检测不同情绪状态受到了研究人员更多的关注，并且提出了几种特征提取方法和分类器来从脑电信号中识别情绪。在本研究中，我们介绍了一种情绪识别系统，该系统使用自回归（AR）模型、顺序前向特征选择（SFS）和K近邻（KNN）分类器，利用情绪视听诱导期间的脑电信号。本文的主要目的是研究AR特征在情绪状态分类中的性能。为实现这一目标，使用了基于莱文森 - 杜宾递归算法的一种著名的AR方法（伯格方法），并提取AR系数作为特征向量。下一步，使用基于SFS算法和戴维斯 - 布尔丁指数的两种不同特征选择方法，以降低计算复杂度和特征冗余；然后，使用三种不同的分类器，包括KNN、二次判别分析和线性判别分析，来区分价态和唤醒水平的两类和三类不同类别。所提出的方法使用来自用于情绪分析的可用数据库的脑电信号进行评估，这些信号是在40次1分钟的视听诱导期间从32名参与者记录的。根据结果，AR特征对于从脑电信号中识别情绪状态是有效的，并且在区分两类和三类价态/唤醒类别方面，KNN的表现优于其他两种分类器。结果还表明，与基于戴维斯 - 布尔丁的特征选择相比，SFS方法将准确率提高了近10 - 15%。对于价态和唤醒的两类，最佳准确率分别为72.33%和74.20%，对于三类，最佳准确率分别为61.10%和65.16%。
