Hou Hui-Rang, Zhang Xiao-Nei, Meng Qing-Hao
Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
J Neurosci Methods. 2020 Jan 21;334:108599. doi: 10.1016/j.jneumeth.2020.108599.
Emotion recognition plays a key role in multimedia. To enhance the sensation of reality, smell has been incorporated into multimedia systems because it can directly stimulate memories and trigger strong emotions.
For the recognition of olfactory-induced emotions, this study explored a combination method using a support vector machine (SVM) with an average frequency band division (AFBD) method, where the AFBD method was proposed to extract the power-spectral-density (PSD) features from electroencephalogram (EEG) signals induced by smelling different odors. The so-called AFBD method means that each PSD feature was calculated based on equal frequency bandwidths, rather than the traditional EEG rhythm-based bandwidth. Thirteen odors were used to induce olfactory EEGs and their corresponding emotions. These emotions were then divided into two types of emotions, pleasure and disgust, or five types of emotions that were very unpleasant, slightly unpleasant, neutral, slightly pleasant, and very pleasant.
Comparison between the proposed SVM plus AFBD method and other methods found average accuracies of 98.9 % and 88.5 % for two- and five-emotion recognition, respectively. These values were considerably higher than those of other combination methods, such as the combinations of AFBD or EEG rhythm-based features with naive Bayesian, k-nearest neighbor classification, voting-extreme learning machine, and backpropagation neural network methods.
The SVM plus AFBD method represents a useful contribution to olfactory-induced emotion recognition. Classification of the five-emotion categories was generally inferior to the classification of the two-emotion categories, suggesting that the recognition performance decreased as the number of emotions in the category increased.
情感识别在多媒体中起着关键作用。为了增强现实感，气味已被纳入多媒体系统，因为它可以直接刺激记忆并引发强烈的情感。
为了识别嗅觉诱发的情感，本研究探索了一种将支持向量机（SVM）与平均频带划分（AFBD）方法相结合的方法，其中AFBD方法被用于从闻不同气味诱发的脑电图（EEG）信号中提取功率谱密度（PSD）特征。所谓的AFBD方法是指每个PSD特征是基于相等的频率带宽计算的，而不是传统的基于脑电节律的带宽。使用13种气味诱发嗅觉脑电及其相应的情感。然后将这些情感分为愉悦和厌恶两种情感类型，或者分为非常不愉快、稍微不愉快、中性、稍微愉快和非常愉快五种情感类型。
将所提出的SVM加AFBD方法与其他方法进行比较，发现二分类情感识别和五分类情感识别的平均准确率分别为98.9%和88.5%。这些值显著高于其他组合方法，如AFBD或基于脑电节律的特征与朴素贝叶斯、k近邻分类、投票极限学习机和反向传播神经网络方法的组合。
SVM加AFBD方法对嗅觉诱发的情感识别做出了有益贡献。五分类情感类别的分类通常不如二分类情感类别的分类，这表明随着类别中情感数量的增加，识别性能会下降。
