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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Subject-Independent Emotion Recognition of EEG Signals Based on Dynamic Empirical Convolutional Neural Network
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Subject-Independent Emotion Recognition of EEG Signals Based on Dynamic Empirical Convolutional Neural Network

机译：基于动态经验卷积神经网络的EEG信号的主题无关情感识别

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摘要

Affective computing is one of the key technologies to achieve advanced brain-machine interfacing. It is increasingly concerning research orientation in the field of artificial intelligence. Emotion recognition is closely related to affective computing. Although emotion recognition based on electroencephalogram (EEG) has attracted more and more attention at home and abroad, subject-independent emotion recognition still faces enormous challenges. We proposed a subject-independent emotion recognition algorithm based on dynamic empirical convolutional neural network (DECNN) in view of the challenges. Combining the advantages of empirical mode decomposition (EMD) and differential entropy (DE), we proposed a dynamic differential entropy (DDE) algorithm to extract the features of EEG signals. After that, the extracted DDE features were classified by convolutional neural networks (CNN). Finally, the proposed algorithm is verified on SJTU Emotion EEG Dataset (SEED). In addition, we discuss the brain area closely related to emotion and design the best profile of electrode placements to reduce the calculation and complexity. Experimental results show that the accuracy of this algorithm is 3.53 percent higher than that of the state-of-the-art emotion recognition methods. What's more, we studied the key electrodes for EEG emotion recognition, which is of guiding significance for the development of wearable EEG devices.
机译：情感计算是关键技术，实现先进的脑机接口之一。人们越来越关于人工智能领域的研究方向。情感识别密切相关，情感计算。虽然基于脑电图（EEG）的情感识别吸引了越来越多的关注，在国内外主题无关的情感识别仍然面临着巨大的挑战。我们提出了一种基于鉴于挑战动态卷积实证神经网络（DECNN）主题无关的情感识别算法。结合经验模式分解（EMD）和微分熵（DE）的优点，提出了一种动态差熵（DDE）算法来提取EEG信号的特征。在此之后，所提取的特征DDE通过卷积神经网络（CNN）分类。最后，该算法是在上海交通大学情绪脑电图数据集（SEED）验证。此外，我们讨论密切相关，情感的大脑区域，并设计电极位置的最佳曲线，以减少计算和复杂性。实验结果表明，该算法的精度比的国家的最先进的感情识别方法高百分之3.53。更重要的是，我们研究了脑电图情感识别，这是可穿戴式EEG设备的发展具有指导意义的关键电极。

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