In this paper, we propose a novel deep learning framework, called spatial-temporal recurrent neural network (STRNN), to integrate the feature learning from both spatial and temporal information of signal sources into a unified spatial-temporal dependency model. In STRNN, to capture those spatially co-occurrent variations of human emotions, a multidirectional recurrent neural network (RNN) layer is employed to capture long-range contextual cues by traversing the spatial regions of each temporal slice along different directions. Then a bi-directional temporal RNN layer is further used to learn the discriminative features characterizing the temporal dependencies of the sequences, where sequences are produced from the spatial RNN layer. To further select those salient regions with more discriminative ability for emotion recognition, we impose sparse projection onto those hidden states of spatial and temporal domains to improve the model discriminant ability. Consequently, the proposed two-layer RNN model provides an effective way to make use of both spatial and temporal dependencies of the input signals for emotion recognition. Experimental results on the public emotion datasets of electroencephalogram and facial expression demonstrate the proposed STRNN method is more competitive over those state-of-the-art methods.

In this paper, we propose a novel neural network model, called bi-hemisphere domain adversarial neural network (BiDANN) model, for electroencephalograph (EEG) emotion recognition. The BiDANN model is inspired by the neuroscience findings that the left and right hemispheres of human's brain are asymmetric to the emotional response. It contains a global and two local domain discriminators that work adversarially with a classifier to learn discriminative emotional features for each hemisphere. At the same time, it tries to reduce the possible domain differences in each hemisphere between source and target domains so as to improve the generality of the recognition model. In addition, we also propose an improved version of BiDANN, denoted by BiDANN-S, for subject-independent EEG emotion recognition problem by lowering the influences of the personal information of subjects to the EEG emotion recognition. Extensive experiments on the SEED database are conducted to evaluate the performance of both BiDANN and BiDANN-S. The experimental results have shown that the proposed BiDANN and BiDANN models achieve state-of-the-art performance in the EEG emotion recognition.

Neuroscience study dimond1976differing has revealed the discrepancy of emotion expression between the left and right hemispheres of human brain. Inspired by this study, in this paper, we propose a novel bi-hemispheric discrepancy model (BiHDM) to learn this discrepancy information between the two hemispheres to improve electroencephalograph (EEG) emotion recognition. Concretely, we first employ four directed recurrent neural networks (RNNs) based on two spatial orientations to traverse electrode signals on two separate brain regions. This enables the proposed model to obtain the deep representations of all the EEG electrodes’ signals that keep their intrinsic spatial dependence. Upon this representation, a pairwise subnetwork is designed to explicitly capture the discrepancy information between the two hemispheres and extract higher-level features for final classification. Furthermore, considering the presence of the domain shift between training and testing data, we incorporate a domain discriminator that adversarially induces the overall feature learning module to generate emotion-related but domain-invariant feature representation so as to further promote EEG emotion recognition. Experiments are conducted on three public EEG emotional datasets, in which we evaluate the performance of the proposed BiHDM as well as investigated the important brain areas in emotion expression and explore to use less electrodes to achieve comparable results. These experimental results jointly demonstrate the effectiveness and advantage of the proposed BiHDM model in solving the EEG emotion recognition problem.

In this paper, we propose a novel Electroencephalograph (EEG) emotion recognition method inspired by neuroscience with respect to the brain response to different emotions. The proposed method, denoted by R2G-STNN, consists of spatial and temporal neural network models with regional to global hierarchical feature learning process to learn discriminative spatial-temporal EEG features. To learn the spatial features, a bidirectional long short term memory (BiLSTM) network is adopted to capture the intrinsic spatial relationships of EEG electrodes within brain region and between brain regions, respectively. Considering that different brain regions play different roles in the EEG emotion recognition, a region-attention layer into the R2G-STNN model is also introduced to learn a set of weights to strengthen or weaken the contributions of brain regions. Based on the spatial feature sequences, BiLSTM is adopted to learn both regional and global spatial-temporal features and the features are fitted into a classifier layer for learning emotion-discriminative features, in which a domain discriminator working corporately with the classifier is used to decrease the domain shift between training and testing data. Finally, to evaluate the proposed method, we conduct both subject-dependent and subject-independent EEG emotion recognition experiments on SEED database, and the experimental results show that the proposed method achieves state-of-the-art performance.

Multimodal emotion recognition (MER) refers to the identification and understanding of human emotional states by combining different signals, including—but not limited to—text, speech, and face cues. MER plays a crucial role in the human–computer interaction (HCI) domain. With the recent progression of deep learning technologies and the increasing availability of multimodal datasets, the MER domain has witnessed considerable development, resulting in numerous significant research breakthroughs. However, a conspicuous absence of thorough and focused reviews on these deep learning-based MER achievements is observed. This survey aims to bridge this gap by providing a comprehensive overview of the recent advancements in MER based on deep learning. For an orderly exposition, this paper first outlines a meticulous analysis of the current multimodal datasets, emphasizing their advantages and constraints. Subsequently, we thoroughly scrutinize diverse methods for multimodal emotional feature extraction, highlighting the merits and demerits of each method. Moreover, we perform an exhaustive analysis of various MER algorithms, with particular focus on the model-agnostic fusion methods (including early fusion, late fusion, and hybrid fusion) and fusion based on intermediate layers of deep models (encompassing simple concatenation fusion, utterance-level interaction fusion, and fine-grained interaction fusion). We assess the strengths and weaknesses of these fusion strategies, providing guidance to researchers to help them select the most suitable techniques for their studies. In summary, this survey aims to provide a thorough and insightful review of the field of deep learning-based MER. It is intended as a valuable guide to aid researchers in furthering the evolution of this dynamic and impactful field.

Micro-expression recognition aims to infer genuine emotions that people try to conceal from facial video clips. It is a very challenging task because micro-expressions have a very low intensity and short duration, which makes micro-expressions difficult to observe. Recently, researchers have designed various spatiotemporal descriptors to describe micro-expressions. It is notable that for better capturing the low-intensity facial muscle movement, a fixed spatial division grid, $8\times 8$ for example, is commonly used to partition the facial images into a few facial blocks before extracting descriptors. However, it is hard to choose an ideal division grid for different micro-expression samples because the division grids affect the discriminative ability of spatiotemporal descriptors to distinguish micro-expressions. To address this problem, in this paper, we design a hierarchical spatial division scheme for spatiotemporal descriptor extraction. By using the proposed scheme, it would not be a problem to determine which division grid is most suitable regarding different micro-expression samples. Furthermore, we propose a kernelized group sparse learning (KGSL) model to process hierarchical scheme based spatiotemporal descriptors such that they are more effective for micro-expression recognition tasks. To evaluate the performance of the proposed micro-expression recognition method consisting of the hierarchical scheme based spatiotemporal descriptors and KGSL, extensive experiments are conducted on two public micro-expression databases: CASME II and SMIC. Compared with many recent state-of-the-art approaches, our method achieves more promising recognition results.