Electroencephalography (EEG) emotion recognition is a vital task in affective computing, but its progress is hampered by the scarcity of labeled data and the significant intersubject variability of EEG signals. While data augmentation and meta-transfer learning have been independently explored to address these issues, their effective integration remains a challenge. Traditional augmentation methods, which are applied uniformly, often fail to generalize in cross-subject scenarios and can disrupt the intrinsic task structure of meta-learning. To overcome this, we introduce Dual-Maxup, a novel dualmaximization data augmentation strategy specifically designed for the meta-transfer learning method. Our approach begins by constructing a comprehensive augmentation pool by combining five standard EEG augmentation methods with various metaaugmentation modes. The core of Dual-Maxup is an adversarial dual-stage selection mechanism. In the inner loop, it adaptively chooses the augmented support samples that produce the highest classification loss for the base learner, forcing it to adapt to more challenging variations. In the outer loop, it selects the augmented query samples that incur the highest loss for the meta learner, ensuring that the model's meta-parameters are updated to handle the most difficult evaluation scenarios. This adversarial selection process continuously exposes the model to hard-to-classify samples, thereby maximizing its robustness and generalization ability. We validate our approach through comprehensive experiments on the benchmark DEAP dataset, demonstrating that Dual-Maxup consistently outperforms state-of-the-art methods in cross-subject emotion recognition. The source code is available at https://github.com/qwangwl/DualMaxUp.

Emotion recognition from electroencephalogram (EEG) signals is a key application in brain-computer interfaces (BCIs), but the high dimensionality and noise in EEG data pose significant challenges. Many existing approaches fail to adequately filter irrelevant information or fully capture complex inter-channel relationships and temporal dynamics, leading to suboptimal emotional representation.To address these challenges, we propose ST-GCN, a novel model that integrates spectral and temporal domain features using graph convolution for robust EEG emotion recognition. ST-GCN employs a channel information reconstruction layer, channel aggregation, and temporal feature extraction to learn discriminative representations across EEG channels and time. Evaluated on the DEAP dataset with a cross-validation setup, ST-GCN achieves state-of-the-art performance, with 98.43% accuracy for valence and 98.69% for arousal, demonstrating its effectiveness in EEG-based emotion recognition.

Emotion is a manifestation of human’s internal psychological and physiological reactions. Understanding and recognizing emotions is one of the important ways to understand human behavior and human-computer interaction. However, with the widespread application of deep learning in the field of EEG emotion recognition, the number of parameters and model size have increased accordingly. In this paper, we combined the Bi-hemisphere asymmetry theory and Compact Convolutional Transformer to propose a model named BiCCT to recognize emotions, which has fewer training parameters and can achieve higher recognition performance. We first constructed three different matrices of the recorded EEG information according to the international 10-20 system to preserve the temporal information and spatial information of the EEG signals. Next, we applied an improved Transformer architecture, which achieves fewer model parameters and a lightweight structure through the token pooling module and the Convolutional Tokenizer module. We conducted a set of subject-dependent and a set of subject-dependent shuffle experiments on the DEAP dataset. The first set of experiments used a subject-known movie to predict a completely unknown movie. In the second set of experiments, all the data of the subject were randomly divided into training set and test set. We obtained 67.42% for valence and 67.81% for arousal in the first set of experiments. We achieved 94.41% accuracy in the valence dimension and 95.15% accuracy in the arousal dimension in the second set of experiments. At the same time, our model parameters are only 0.17M, which is far lower than other models. It means that our model is lighter and faster in training speed, and has the ability to be deployed in some scenarios with limited computing resources potential.

Emotion recognition based on EEG (Electroen-cephalogram) has been widely used in may scenarios, such as Brain-computer interface, medical health and entertainment, etc. However, large differences exist in subjects due to individual characteristics, and EEG data of different time periods usually distribute inconsistently, which hinder the further development of EEG-based research. In this paper, we propose a LUR model to partially address these challenges. In the first stage, we extracted candidate features based on neuroscience research and learned them using SVM or Naive Bayes algorithm. If the performance during the validation phase is satisfactory, we keep the features. Otherwise we replace the candidate features and use an online learning method named LUR(Learn, Unlearn, and Relearn), which continuously prunes the irrelevant connections of the current data and retains important connections to constructing a model more suitable for the subject. Because the proposed model is based on streaming data, it can continuously relearn and solve the problem that the input data is not i.i.d. to a certain extent. Experiments were conducted on two public datasets, DEAP and DREAMER, and competitive results were obtained.

Emotion recognition based on EEG has attracted widespread research interest in the field of brain-computer interfaces. To extract EEG intra- and inter-channel features and find discriminative representations for EEG emotion recognition, we propose EEG-Multilayer Perceptron (EEG-MLP) architecture. EEG-MLP is completely composed of MLPs and mainly consists of two modules, one is a temporal mixer that captures intra-channel (temporal) information, and the other is a channel mixer that captures inter-channel information. The two modules learn knowledge in a parallel manner, and then their outputs are fused to extract global information and classify EEG emotions. We conduct extensive experiments on DEAP dataset. EEG-MLP is first compared with five inter-channel interaction models (related to CNN or GCN) to verify its effectiveness. Then, five other models with similar architecture to EEG-MLP were also contrasted. Experimental results show that EEG-MLP achieves the best performance among the above methods, with accuracies of 94.87% and 95.32% in the valence and arousal dimensions, respectively. In addition, it has a strong discrimination ability for complex categories, and has low requirements for storage resources.

Research on human emotions based on EEG during multimedia stimuli is an emerging field that has made significant progress in EEG-based emotion classification. However, current studies often neglect the extraction of dynamic information from EEG signals and lack the exploration of local information. Moreover, many existing models are overly complex, demanding an excessive investment in training resources and time. In this paper, we propose a novel, simple two-stream channel reconstruction and feature attention network, named CRFAE-motionNet, for EEG emotion recognition. The main advantage of CRFAEmotionNet is its ability to simultaneously integrate static and dynamic information from EEG signals within a unified network. Additionally, it can extract continuous EEG temporal information through channel reconstruction and utilize feature attention to further explore local information. The proposed network was evaluated using the publicly available DEAP dataset. The experimental results indicate that the proposed CRFAEmotionNet outperforms the state-of-the-art baselines, achieving the accuracy of 98.7% for valence and 98.6% for arousal.