The emotional state of people plays a key role in physiological and behavioral human interaction. Emotional state analysis entails many fields such as neuroscience, cognitive sciences, and biomedical engineering because the parameters of interest contain the complex neuronal activities of the brain. Electroencephalogram (EEG) signals are processed to communicate brain signals with external systems and make predictions over emotional states. This paper proposes a novel method for emotion recognition based on deep convolutional neural networks (CNNs) that are used to classify Valence, Arousal, Dominance, and Liking emotional states. Hence, a novel approach is proposed for emotion recognition with time series of multi-channel EEG signals from a Database for Emotion Analysis and Using Physiological Signals (DEAP). We propose a new approach to emotional state estimation utilizing CNN-based classification of multi-spectral topology images obtained from EEG signals. In contrast to most of the EEG-based approaches that eliminate spatial information of EEG signals, converting EEG signals into a sequence of multi-spectral topology images, temporal, spectral, and spatial information of EEG signals are preserved. The deep recurrent convolutional network is trained to learn important representations from a sequence of three-channel topographical images. We have achieved test accuracy of 90.62% for negative and positive Valence, 86.13% for high and low Arousal, 88.48% for high and low Dominance, and finally 86.23% for like–unlike. The evaluations of this method on emotion recognition problem revealed significant improvements in the classification accuracy when compared with other studies using deep neural networks (DNNs) and one-dimensional CNNs.

中文翻译：

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深度卷积神经网络的基于EEG的情绪识别

人的情绪状态在人的生理和行为互动中起着关键作用。情绪状态分析涉及许多领域，例如神经科学，认知科学和生物医学工程，因为感兴趣的参数包含大脑的复杂神经元活动。脑电图（EEG）信号经过处理，可将大脑信号与外部系统进行通讯，并对情绪状态做出预测。本文提出了一种基于深度卷积神经网络（CNN）的情感识别新方法，该方法用于对价态，唤醒，优势和喜欢状态进行分类。因此，提出了一种新颖的方法来进行情绪识别，该方法利用来自情绪分析数据库和使用生理信号（DEAP）的多通道脑电信号的时间序列进行识别。我们提出了一种新的情感状态估计方法，该方法利用了基于CNN的从EEG信号获得的多光谱拓扑图像的分类。与大多数消除EEG信号的空间信息的基于EEG的方法相反，将EEG信号转换为多光谱拓扑图像序列可以保留EEG信号的时间，光谱和空间信息。深度循环卷积网络经过训练，可以从三通道地形图序列中学习重要的表示形式。对于负价和正价，我们达到了90.62％的测试准确度，对高和低Arousal达到了86.13％，对高和低优势占据了88.48％，对于不喜欢时最终达到了86.23％。