An Yanling, Hu Shaohai, Duan Xiaoying, Zhao Ling, Xie Caiyun, Zhao Yingying
Institute of Information Science, Beijing Jiaotong University, Beijing, China.
School of Economics and Management, Northwest University, Xi'an, China.
Front Comput Neurosci. 2021 Oct 18;15:743426. doi: 10.3389/fncom.2021.743426. eCollection 2021.
As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are used for emotion recognition in academic and industrial circles. In order to overcome the disadvantage that traditional machine learning based emotion recognition technology relies too much on a manual feature extraction, we propose an EEG emotion recognition algorithm based on 3D feature fusion and convolutional autoencoder (CAE). First, the differential entropy (DE) features of different frequency bands of EEG signals are fused to construct the 3D features of EEG signals, which retain the spatial information between channels. Then, the constructed 3D features are input into the CAE constructed in this paper for emotion recognition. In this paper, many experiments are carried out on the open DEAP dataset, and the recognition accuracy of valence and arousal dimensions are 89.49 and 90.76%, respectively. Therefore, the proposed method is suitable for emotion recognition tasks.
作为情感计算的关键技术之一，情感识别受到了广泛关注。脑电图（EEG）信号具有自发性且难以伪装，因此在学术界和工业界被用于情感识别。为了克服基于传统机器学习的情感识别技术过于依赖手工特征提取的缺点，我们提出了一种基于三维特征融合和卷积自动编码器（CAE）的脑电情感识别算法。首先，融合脑电信号不同频段的微分熵（DE）特征，构建脑电信号的三维特征，保留通道间的空间信息。然后，将构建好的三维特征输入本文构建的CAE进行情感识别。本文在公开的DEAP数据集上进行了大量实验，效价和唤醒度维度的识别准确率分别为89.49%和90.76%。因此，所提方法适用于情感识别任务。
