A Novel Convolutional Neural Network for Emotion Recognition Using Neurophysiological Signals
A Novel Convolutional Neural Network for Emotion Recognition Using Neurophysiological Signals
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DOI：
10.1109/icra46639.2022.9811868
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Marc Tunnell;Huijin Chung;Yuchou Chang
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作者：
Marc Tunnell;Huijin Chung;Yuchou Chang
非侵入性脑机接口(BCI)为我们提供了一种独特的能力，只使用神经生理信号来分类一个人的心理状态，例如那些通过脑电(EEG)捕捉到的信号。凭借这种能力，医疗保健领域出现了新的创新途径，特别是在机器人技术应用方面。EEGNet是一种新的用于脑电信号分类的深度学习技术，训练集有限，可以很好地推广到各种BCI范型，并且其性能可以进一步提高。我们提出了在EEG-BCI分类流水线中使用Thomson多纸功率谱密度估计以及一种新的卷积神经网络(CNN)，该网络利用有效的正则化可分离卷积产生的稀疏特征映射来扩展EEGNet。进一步，我们测试了散布高斯噪声作为数据增强技术的有效性。为了展示这一新管道的改进，我们在一个广泛使用的与情绪分类相关的公共脑电数据集上进行了测试，然后进行了消融研究，以确定最主要的影响因素。在此公开数据集上的准确率为77.16%。这些结果表明，与最先进的分类方法相比，我们的流水线提高了10.86%的分类准确率。
Non-invasive brain-computer interfaces (BCIs) provide us with the unique ability to classify the psychological state of a person using only neurophysiological signals, such as those captured with an electroencephalogram (EEG). With this ability, new avenues for innovation in the field of healthcare arise, especially as it is used for robotics. EEGNet is a novel deep learning technique for the classification of EEG data with a limited training set that generalizes well to a variety of BCI paradigms, and the performance thereof can further be improved. We propose the use of Thomson Multitaper Power Spectral Density estimation in the EEG-BCI classification pipeline as well as a novel convolutional neural network (CNN), which extends EEGNet with sparse feature maps produced by efficient regularized separable convolutions. Further, we test the efficacy of interspersed Gaussian noise as a data augmentation technique. To show the improvements found with this new pipeline, we test on a widely used public EEG dataset related to emotion classification, then perform an ablation study to determine the most contributing factors. The accuracy on this public dataset was 77.16%. These results show that our pipeline improved the classification accuracy by 10.86% when compared with the state-of-the-art.