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首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >A novel convolutional neural networks for emotion recognition based on EEG signal
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A novel convolutional neural networks for emotion recognition based on EEG signal

机译：基于脑电信号的新型卷积神经网络情感识别

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摘要

Emotion recognition based on electroencephalogram (EEG) signal is attracting more and more attention. Many feature engineering based models have been investigated. However, these models require a lot of effort for manually designing feature set. And these features can be hardly transformed among different problems. To reduce the manual effort on features used in EEG-based recognition and improve the performance, we propose an end-to-end model which is based on Convolutional Neural Networks (CNNs). In order to represent the EEG signals better, the original channels of EEG are firstly rearranged by Pearson Correlation Coefficient and the rearranged EEGs are fed into CNN. experiments were carried on DEAP dataset. The experimental results on the DEAP dataset show that the proposed method achieves 77.98% accuracy on the Valence recognition and 72.98% on the Arousal recognition.
机译：基于脑电图（EEG）信号的情感识别越来越受到人们的关注。已经研究了许多基于特征工程的模型。但是，这些模型需要大量的精力来手动设计功能集。这些功能很难在不同问题之间转换。为了减少基于EEG的识别中使用的功能上的人工工作并提高性能，我们提出了一种基于卷积神经网络（CNN）的端到端模型。为了更好地表示EEG信号，首先通过Pearson相关系数重新排列EEG的原始通道，并将重新排列的EEG馈入CNN。实验在DEAP数据集上进行。在DEAP数据集上的实验结果表明，该方法在价数识别上的准确度达到77.98％，在Arousal识别上的准确度达到72.98％。

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