基于有记忆递归神经网络的脑电特征情感识别研究

摘要：

为了提高脑电信号多分类的情感识别率,文中选用上海交通大学提供的SEED脑电信号数据集,对其进行分频带特征提取。将脑电数据的微分熵特征、微分不对称性特征和有理不对称性特征通过线性动力系统平滑特征后,与功率谱密度特征进行分类效果比较,再利用有记忆递归神经网络的方法进行分类,发现提取的微分熵特征经过分类的效果好。在对3种情感进行分类的过程中,采用长短时记忆神经网络分类相比于其他机器学习方法识别率有所提高,情感识别的平均准确率可达到95.045 9%。

关键词: 脑电信号, SEED数据集, 微分熵, 微分不对称, 有理不对称, 线性动力系统, 有记忆递归神经网络

Abstract:

In order to improve the accuracy rate of the emotional recognition of EEG signals in multi-classification, the SEED dataset published by SJTU is selected as the sample of EEG dataset. The original EEG signal is divided into five frequency bands, and their features are extracted. After the features of the differential entropy, the differential asymmetry and the rational asymmetry of EEG datasets are smoothed by linear dynamic system, the classification effect is compared with the feature of power spectral density. Then, the method of the long short-term memory neural network is used to classify emotion. It is concluded that the classification of the differential entropy feature is effective. Finally, compared with other machine learning methods, the recognition rate is improved, and the average accuracy of emotion recognition reaches 95.045 9%.

Key words: EEG signals, SEED dataset, differential entropy, differential asymmetry, rational asymmetry, linear dynamical system, long short-term memory neural network

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