Yang Haihui, Huang Shiguo, Guo Shengwei, Sun Guobing
College of Electronic Engineering, Heilongjiang University, Harbin 150080, China.
Key Laboratory of Information Fusion Estimation and Detection, Harbin 150080, China.
Entropy (Basel). 2022 May 16;24(5):705. doi: 10.3390/e24050705.
With the widespread use of emotion recognition, cross-subject emotion recognition based on EEG signals has become a hot topic in affective computing. Electroencephalography (EEG) can be used to detect the brain's electrical activity associated with different emotions. The aim of this research is to improve the accuracy by enhancing the generalization of features. A Multi-Classifier Fusion method based on mutual information with sequential forward floating selection (MI_SFFS) is proposed. The dataset used in this paper is DEAP, which is a multi-modal open dataset containing 32 EEG channels and multiple other physiological signals. First, high-dimensional features are extracted from 15 EEG channels of DEAP after using a 10 s time window for data slicing. Second, MI and SFFS are integrated as a novel feature-selection method. Then, support vector machine (SVM), k-nearest neighbor (KNN) and random forest (RF) are employed to classify positive and negative emotions to obtain the output probabilities of classifiers as weighted features for further classification. To evaluate the model performance, leave-one-out cross-validation is adopted. Finally, cross-subject classification accuracies of 0.7089, 0.7106 and 0.7361 are achieved by the SVM, KNN and RF classifiers, respectively. The results demonstrate the feasibility of the model by splicing different classifiers' output probabilities as a portion of the weighted features.
随着情感识别的广泛应用，基于脑电信号的跨主体情感识别已成为情感计算领域的研究热点。脑电图（EEG）可用于检测与不同情绪相关的大脑电活动。本研究旨在通过增强特征的泛化能力来提高准确率。提出了一种基于互信息与顺序前向浮动选择的多分类器融合方法（MI_SFFS）。本文使用的数据集是DEAP，它是一个多模态开放数据集，包含32个脑电通道和多个其他生理信号。首先，在使用10秒时间窗口对数据进行切片后，从DEAP的15个脑电通道中提取高维特征。其次，将互信息和顺序前向浮动选择集成作为一种新颖的特征选择方法。然后，使用支持向量机（SVM）、k近邻（KNN）和随机森林（RF）对正负情绪进行分类，以获得分类器的输出概率作为加权特征用于进一步分类。为了评估模型性能，采用留一法交叉验证。最后，SVM、KNN和RF分类器的跨主体分类准确率分别达到0.7089、0.7106和0.7361。结果表明，将不同分类器的输出概率拼接作为加权特征的一部分，该模型是可行的。
