基于脑电图的新型情绪识别特征提取方法

基于脑电图(EEG)信号对情感识别计算进行研究.针对脑电图的特征提取难和模型计算难的问题, 提出了一种从EEG信号中获得可靠区别特征的创新方法.该方法将微分熵与线性判别分析(LDA)相结合，可被应用于情绪EEG信号的特征提取.采用3类情绪EEG数据集进行实验，结果表明该方法能够有效提高EEG分类的性能:与原始数据集的结果相比，平均准确度提高了68%，比单独使用微分熵进行特征提取时的准确度高7%.总执行时间结果证明提出的方法具有较低的时间复杂度.研究结果在3类情感脑电图识别领域具有重要的实用价值，可被应用于实际的工程领域.

A New Method for Feature Extraction in Emotion Recognition Based on EEG

Emotional calculation is studied by means of electroencephalogram (EEG) signals. Aiming to solve the problem of difficulty in extracting EEG signal features and building large computational models, an innovative method is proposed for obtaining reliable distinctive features from EEG signals. The feature extraction method combines differential entropy with linear discriminant analysis (LDA) and can be applied to feature extraction of emotional EEG signals. Three types of emotional EEG data sets are used to conduct experiments. Experimental results show that the feature extraction method can effectively improve the performance of the EEG classification: the average accuracy is improved by 68% compared to the results of the original data set, which is 7% higher than the result obtained by feature extraction using only differential entropy. The total execution time indicates that the proposed method has a lower time complexity. The research results have important practical significance in the field of three-category EEG emotion recognition and can be effectively applied to the actual engineering field.