基于SVM-KNN算法的情绪脑电识别

情绪识别与日常生活的诸多领域都有很大联系.然而,通过单一算法难以获得较高的情绪识别准确率,为此,提出一种基于支持向量机(support vector machine,SVM)和K近邻(K-nearest neighbors,KNN)融合算法(SVM-KNN)的情绪脑电识别模型.在情绪分类时,首先计算待识别样本与最优分类...

Emotion Classification Using EEG Signals Based on SVM-KNN Algorithm

Emotion recognition is closely related to many facets of our daily lives. However, it is difficult to achieve a satisfying emotion recognition rate by using one single algorithm. Therefore, this study puts forward an emotion recognition model based on electroencephalogram (EEG) with a fusion algorithm that combines the support vector machine (SVM) algorithm with the K-nearest neighbors algorithm (SVM-KNN). In the emotion classification process, the spatial distance between the sample to be identified and the optimal classification hyperplane is calculated. If it is longer than the preset threshold, the SVM classifier is chosen to classify the emotion records. Otherwise, the KNN classifier is chosen. Finally, experiments are carried out on the SJTU emotion EEG dataset (SEED). The comparative experiments show that the SVM-KNN algorithm improves the accuracy of the three-emotion classification. This model can effectively identify the types of emotions and thus has positive significance in obtaining the emotions of patients with expression disorders in medical care.