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Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

机译：利用人工神经网络在自动情感识别中提取脑电信号特征的滤波双谱技术

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The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG) signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN) as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN) as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN), and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction method is suitable for use in an EEG-based emotion recognition system.
机译：由于在诸如情感计算和生物医学工程各个领域的多种应用中实现自动情感检测系统的可能性越来越高，因此自动情感检测系统的开发最近受到了广泛的关注。脑电图（EEG）信号优于面部表情，因为人们无法控制大脑产生的EEG信号。脑电图确保心理信号的可靠性更高。然而，由于其在个体之间的独特性以及对噪声的脆弱性，EEG信号的使用可能相当复杂。在本文中，我们提出了一种方法，该方法通过使用过滤的双谱作为特征提取子系统并使用人工神经网络（ANN）作为分类器来进行基于EEG的情绪识别。理论上，双谱优于功率谱，因为它可以识别EEG信号的非线性过程分量之间的相位耦合。在特征提取过程中，要提取包含在双谱矩阵中的信息，可使用3D金字塔过滤器对双谱值进行采样和量化。实验结果表明，来自5×5非重叠3D金字塔滤镜的双光谱值的平均百分比产生了最高的识别率。我们发现将脑额叶区域的EEG通道数量减少到仅8个不会显着影响识别率，然后增加训练过程中使用的数据样本数量以提高系统的识别率。我们还利用了概率神经网络（PNN）作为另一个分类器，并将其识别率与反向传播神经网络（BPNN）的识别率进行了比较，结果表明该PNN产生了可比的识别率，并且计算成本较低。我们的研究表明，使用3D滤波作为特征提取方法提取的EEG信号的双谱值适用于基于EEG的情绪识别系统。

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