Emotion is interpreted as a psycho-physiological process, and it is associated with personality, behavior, motivation, and character of a person. The objective of affective computing is to recognize different types of emotions for human-computer interaction (HCI) applications. The spatiotemporal brain electrical activity is measured using multi-channel electroencephalogram (EEG) signals. Automated emotion recognition using multi-channel EEG signals is an exciting research topic in cognitive neuroscience and affective computing. This paper proposes the rhythm-specific multi-channel convolutional neural network (CNN) based approach for automated emotion recognition using multi-channel EEG signals. The delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) rhythms of EEG signal for each channel are evaluated using band-pass filters. The EEG rhythms from the selected channels coupled with deep CNN are used for emotion classification tasks such as low-valence (LV) vs. high valence (HV), low-arousal (LA) vs. high-arousal (HA), and low-dominance (LD) vs. high dominance (HD) respectively. The deep CNN architecture considered in the proposed work has eight convolutions, three average pooling, four batch-normalization, three spatial drop-outs, two drop-outs, one global average pooling and, three dense layers. We have validated our developed model using three publicly available databases: DEAP, DREAMER, and DASPS. The results reveal that the proposed multivariate deep CNN approach coupled with β-rhythm has obtained the accuracy values of $98.91\text{\%}$, $98.45\text{\%}$, and $98.69\text{\%}$ for LV vs. HV, LA vs. HA, and LD vs. HD emotion classification strategies, respectively using DEAP database with 10-fold cross-validation (CV) scheme. Similarly, the accuracy values of $98.56\text{\%}$, $98.82\text{\%}$, and $98.99\text{\%}$ are obtained for LV vs. HV, LA vs. HA, and LD vs. HD classification schemes, respectively, using deep CNN and θ-rhythm. The proposed multi-channel rhythm-specific deep CNN classification model has obtained the average accuracy value of 57.14% using α-rhythm and trial-specific CV using DASPS database. Moreover, for 8-quadrant based emotion classification strategy, the deep CNN based classifier has obtained an overall accuracy value of 24.37% using γ-rhythms of multi-channel EEG signals. Our developed deep CNN model can be used for real-time automated emotion recognition applications.

情感被解释为一种心理生理过程，并且与一个人的性格，行为，动机和性格相关。情感计算的目的是为人机交互（HCI）应用程序识别不同类型的情绪。使用多通道脑电图（EEG）信号测量时空脑电活动。使用多通道EEG信号的自动情感识别是认知神经科学和情感计算领域一个令人兴奋的研究主题。本文提出了一种基于节奏的多通道卷积神经网络（CNN）的方法，该方法使用多通道EEG信号进行自动情感识别。δ（δ），θ（θ），α（α），β（β），并使用带通滤波器评估每个通道的EEG信号的伽玛（γ）节奏。来自选定频道的EEG节奏与深层CNN一起用于情感分类任务，例如低价（LV）与高价（HV），低金枪鱼（LA）与高金枪鱼（HA）和低价-优势（LD）与高优势（HD）。拟议工作中考虑的深度CNN架构具有8个卷积，3个平均池，4个批归一化，3个空间缺失，2个缺失，1个全局平均池和3个密集层。我们已经使用三个公共数据库验证了我们开发的模型：DEAP，DREAMER和DASPS。结果表明，所提出的多元深度CNN方法与β耦合-节奏已获得的精度值 $98.91\text{％}$， $98.45\text{％}$， 和 $98.69\text{％}$分别使用带有10倍交叉验证（CV）方案的DEAP数据库分别针对LV与HV，LA与HA，LD与HD情感分类策略进行比较。同样，精度值$98.56\text{％}$， $98.82\text{％}$， 和 $98.99\text{％}$分别使用深CNN和θ-节奏分别针对LV vs.HV，LA vs.HA和LD vs.HD分类方案获得了A. 所提出的多通道节奏特有的深度CNN分类模型使用α-节奏获得了平均准确度值为57.14％，使用DASPS数据库获得了特定于试验的CV。此外，对于基于8象限的情感分类策略，基于深CNN的分类器使用多通道EEG信号的γ节奏获得了24.37％的整体准确度值。我们开发的深度CNN模型可用于实时自动情感识别应用程序。