基于核谱回归与随机森林的脑电情感识别

利用DEAP情感数据库研究脑电的情感识别问题。首先，使用聚类算法确定情感状态的目标类别；然后，比较了两种不同的特征提取方法：一种是小波变换，另一种是非线性动力学，并研究了基线特征对情感分类效果的影响；最后，研究了5种特征降维方法对分类性能的影响，同时比较了4种不同分类器的性能，包括K-最近邻（KNN）、朴素贝叶斯（NB）、支持向量机（SVM）和随机森林（RF）。研究结果表明，核谱回归（KSR）降维方法和随机森林分类器的组合对情感状态的分类效果最好。通过对脑区与情感关系的研究发现，只使用部分脑区的少量电极也可以达到90%的分类准确度，这些电极主要分布在额叶皮层。

Emotion Recognition of EEG Based on Kernel Spectral Regression and Random Forest Algorithm

With the development of computer technology and human-computer interaction technology, the emotion recognition based on EEG has attracted more and more attention. Because researchers often use different stimuli and emotional categories, it is difficult to compare these results in different emotion recognition works. In this paper, we investigate emotion recognition problem via DEAP database. Firstly, we use data clustering technique to determine target classes of human emotional state. And then, we compare two different feature extraction methods, i.e., wavelet transform and nonlinear dynamics, and analyze the effect of baseline features on emotion classification. In addition, we examine five feature reduction algorithms via classification performance and compare the performance of four different classifiers, including K-nearest neighbor, naïve Bayesian, support vector machine, and random forest. It is shown from the results that the combination of kernel spectral regression (KSR) and random forest (RF) can attain the best classifying quality for emotion recognition. Finally, it is found from the study on the relationship between brain regions and emotion that only using a small number of channels, mainly distributed in the frontal cortex, can also achieve a relatively good classification accuracy.