Emotions are a critical aspect of daily life and serve a crucial role in human decision-making, planning, reasoning, and other mental states. As a result, they are considered a significant factor in human interactions. Human emotions can be identified through various sources, such as facial expressions, speech, behavior (gesture/position), or physiological signals. The use of physiological signals can enhance the objectivity and reliability of emotion detection. Compared with peripheral physiological signals, electroencephalogram (EEG) recordings are directly generated by the central nervous system and are closely related to human emotions. EEG signals have the great spatial resolution that facilitates the evaluation of brain functions, making them a popular modality in emotion recognition studies. Emotion recognition using EEG signals presents several challenges, including signal variability due to electrode positioning, individual differences in signal morphology, and lack of a universal standard for EEG signal processing. Moreover, identifying the appropriate features for emotion recognition from EEG data requires further research. Finally, there is a need to develop more robust artificial intelligence (AI) including conventional machine learning (ML) and deep learning (DL) methods to handle the complex and diverse EEG signals associated with emotional states. This paper examines the application of DL techniques in emotion recognition from EEG signals and provides a detailed discussion of relevant articles. The paper explores the significant challenges in emotion recognition using EEG signals, highlights the potential of DL techniques in addressing these challenges, and suggests the scope for future research in emotion recognition using DL techniques. The paper concludes with a summary of its findings. 

Emotion recognition in EEG signals using deep learning methods: A review

TC-Net: A Transformer Capsule Network for EEG-based emotion recognition

Graph convolutional networks (GCNs) have shown great prowess in learning topological relationships among electroencephalogram (EEG) channels for EEG-based emotion recognition. However, most existing GCN-only methods are designed with a single spatial pattern, lacking connectivity enhancement within local functional regions and ignoring the data dependencies of EEG original data. In this article, hierarchical dynamic GCN (HD-GCN) is proposed to explore dynamic multilevel spatial information among EEG channels, with discriminative features of EEG signals as auxiliary information. Specifically, representation learning in topological space consists of two branches: one for extracting global dynamic information and one for exploring augmentation information in local functional regions. In each branch, a layerwise adjacency matrix is utilized to enrich the expressive power of GCN. Furthermore, a data-dependent auxiliary information module (AIM) is developed to capture multidimensional fusion features. Extensive experiments on two public datasets, SJTU emotion EEG dataset (SEED) and DREAMER, demonstrate that the proposed method consistently exceeds state-of-the-art methods. Interpretability analysis of the proposed model is performed, discovering the active brain regions and important electrode pairs related to emotion.

Hierarchical Dynamic Graph Convolutional Network With Interpretability for EEG-Based Emotion Recognition.

As a type of recurrent neural networks (RNNs) modeled as dynamic systems, the gradient neural network (GNN) is recognized as an effective method for static matrix inversion with exponential convergence. However, when it comes to time-varying matrix inversion, most of the traditional GNNs can only track the corresponding time-varying solution with a residual error, and the performance becomes worse when there are noises. Currently, zeroing neural networks (ZNNs) take a dominant role in time-varying matrix inversion, but ZNN models are more complex than GNN models, require knowing the explicit formula of the time-derivative of the matrix, and intrinsically cannot avoid the inversion operation in its realization in digital computers. In this article, we propose a unified GNN model for handling both static matrix inversion and time-varying matrix inversion with finite-time convergence and a simpler structure. Our theoretical analysis shows that, under mild conditions, the proposed model bears finite-time convergence for time-varying matrix inversion, regardless of the existence of bounded noises. Simulation comparisons with existing GNN models and ZNN models dedicated to time-varying matrix inversion demonstrate the advantages of the proposed GNN model in terms of convergence speed and robustness to noises. 

GNN Model for Time-Varying Matrix Inversion With Robust Finite-Time Convergence

Due to inter-individual variances, cross-subject electroencephalogram (EEG)-based emotion recognition is a challenging task. In this paper, we construct a multi-branch Capsule network (named DA-CapsNet) based on domain adaptation to improve the performance of cross-subject EEG emotion recognition. To fully capture the various intensity characteristics of a single emotion, firstly, DA-CapsNet decomposes the source and the target domain EEG signals into four frequency bands and homomorphically groups the data in each band, and then extracts the differential entropy (DE) features for each group separately. Taking into account the spatial arrangement of the electrodes, the DE features are mapped into a two-dimensional matrix to form a homomorphic difference cube sequence (HDCS). Second, to enhance the feature information of the same emotion and accelerate the run efficiency of the network, a parallel structured multi-branch primary Capsual network (CapsNet) is constructed in this paper. The multi-branch primary CapsNet can effectively extract the aforementioned sequence discriminative features and fuse them as the input features of the capsule emotion classifier. Finally, to lessen inter-domain distribution discrepancies, we brought adversarial domain adaptation to improve the performance of cross-subject emotion recognition. Numerous tests are run on the three public datasets of EEG, and the results show that the proposed algorithm in this paper works well. 

DA-CapsNet: A Multi-Branch Capsule Network Based on Adversarial Domain Adaption for Cross-Subject EEG Emotion Recognition

Electroencephalogram (EEG) data is commonly applied in the emotion recognition research area. It can accurately reflect the emotional changes of the human body by applying graphical-based algorithms or models. EEG signals are non-linear signals. Biological tissues’ adjustment and adaptive ability will inevitably affect electrophysiological signals, making EEG have the typical non-linear characteristics. Graph Convolutional Broad Network (GCB-net) extracted features from non-linear signals and abstract features via stacked CNN. It adopted the broad concept and enhanced the feature by the broad learning system (BLS), obtaining sound results. However, it performed poorly with increasing network depth, and the accuracy of some features decreased with BLS. This paper proposed a Residual Graph Convolutional Broad Network (Residual GCB-net), which promotes the performance on a deeper-layer network and extracts higher-level information. It substitutes the original convolutional layer with residual learning blocks, which solves the deep learning network degradation and extracts more features in deeper networks. In SEED data set, GCB-Res net could obtain the best accuracy (94.56%) on the all-frequency band of differential entropy (DE) and promote much on another feature. In Dreamer, it obtained the best accuracy (91.55%) on the dimension of Arousal. The result demonstrated the excellent classification performance of Residual GCB-net in EEG emotion recognition. 

Residual GCB-Net: Residual Graph Convolutional Broad Network on Emotion Recognition

Convolutional Neural Networks (CNNs) have exhibited their great power in a variety of vision tasks. However, the lack of transform-invariant property limits their further applications in complicated real-world scenarios. In this work, we proposed a novel generalized one dimension convolutional operator (OneDConv), which dynamically transforms the convolution kernels based on the input features in a computationally and parametrically efficient manner. The proposed operator can extract the transform-invariant features naturally. It improves the robustness and generalization of convolution without sacrificing the performance on common images. The proposed OneDConv operator can substitute the vanilla convolution, thus it can be incorporated into current popular convolutional architectures and trained end-to-end readily. On several popular benchmarks, OneDConv outperforms the original convolution operation and other proposed models both in canonical and distorted images.

pdf/onedconv-generalized-convolution-for-transform-invariant-rfqykqoy.pdf

OneDConv: Generalized Convolution For Transform-Invariant Representation

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Residual GCB-Net: Residual Graph Convolutional Broad Network on Emotion Recognition

OneDConv: Generalized Convolution For Transform-Invariant Representation