基于脑电图的深度卷积神经网络情感识别

Hui-Min Shao, Jianguo Wang, Yu Wang, Yuan Yao, Junjiang Liu
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引用次数: 7

摘要

情绪与人们的工作和生活密切相关。情感分析与识别不仅是解决某些精神疾病的迫切需要,在人机交互、娱乐、医疗等领域也有着广阔的应用前景。因此,对情绪脑电信号进行分类具有重要的意义。本文将卷积神经网络(CNN)引入到情绪脑电图识别过程中。该方法的创新之处在于调整CNN的卷积核以适应脑电信号的输入。对三分类情绪脑电信号的分类精度达到0.8579。
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EEG-Based Emotion Recognition with Deep Convolution Neural Network
Emotions are closely related to people's work and life. Emotional analysis and recognition is not only an urgent need to solve certain mental illnesses, but also has broad application prospects in the fields of human-computer interaction, entertainment and medical care. Therefore, it is of great value to classify emotional EEG signals. This paper introduces CNN(Convolutional Neural Networks)into the process of emotional EEG recognition. The innovation of this method is to adjustthe convolution kernel of the CNN to adapt to the input of EEG signals. The classification accuracy of 0.8579 is achieved in the three-classification emotional EEG signal.
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