EEG-based emotion recognition is crucial for understanding human affective states, offering valuable insights into diverse fields like mental health monitoring and humancomputer interaction. Recent advancements in graph learning have significantly impacted EEG emotion recognition due to their ability to model the complex, dynamic relationships within brain networks. However, current methods often neglect the interplay between shared and individual correlations among EEG channels. Furthermore, individual variations in EEG patterns lead to distributional shifts that hinder the generalization of existing approaches. This paper proposes a novel Two-stage domain Adaptation with Hybrid Adaptive Graph learning (TAHAG) for EEG emotion recognition. TAHAG first employs hybrid adaptive graph learning to capture both shared and individual spatial characteristics of the EEG signals, dynamically integrating their contributions. Feature attention mechanisms are then incorporated to refine node features and enhance the model's discriminability. To address distributional variations, TAHAG utilizes a two-stage domain adaptation strategy. This strategy involves aligning the refined node features across different domains through discrepancy alignment. Subsequently, adversarial training captures domain-invariant summarized features of the entire graph. Extensive experiments on three public datasets demonstrate the superiority of TAHAG compared to existing methods. Furthermore, visualization of neuronal activity reveals significant brain regions and inter-channel relationships relevant to EEG emotion recognition.

Fatigue driving is a significant cause of road traffic accidents and associated casualties. Automatic assessment of driver drowsiness by monitoring electroencephalography (EEG) signals offer a more objective way to improve driving safety. However, most existing measures are based on multi-channel EEG signals, which are more difficult to apply in practical scenarios as it usually lacks better portability and comfort. In addition, due to the relatively parsimonious and non-stationary characteristics, it is still challenging to effectively accomplish drowsiness recognition by exploiting single-channel EEG signals alone. To this end, we propose a novel temporal-frequential attentional convolutional neural network (TFAC-Net) to take full advantage of spectral-temporal features for single-channel EEG driver drowsiness recognition. Specifically, to capture the potentially valuable information contained in single-channel EEG, the continuous wavelet transform is first employed to generate a corresponding spectral-temporal representation. Then, the temporal-frequential attention mechanism is adopted to reveal critical time-frequency regions in terms of the driver’s mental state. Finally, an adaptive feature fusion module is considered to recalibrate and integrate the most relevant feature channels for final prediction. Extensive experimental results on a widely used public EEG driving dataset demonstrate that the TFAC-Net approach is superior to the state-of-the-art methods, and could discover some discriminative temporal-frequential regions. Moreover, this study also sheds light on the development of portable EEG devices and practical driver drowsiness recognition.

Emotion recognition based on electroencephalography (EEG) has attracted significant attention and achieved considerable advances in the fields of affective computing and human-computer interaction. However, most existing studies ignore the coupling and complementarity of complex spatiotemporal patterns in EEG signals. Moreover, how to exploit and fuse crucial discriminative aspects in high redundancy and low signal-to-noise ratio EEG signals remains a great challenge for emotion recognition. In this paper, we propose a novel attention-based spatial-temporal dual-stream fusion network, named ASTDF-Net, for EEG-based emotion recognition. Specifically, ASTDF-Net comprises three main stages: first, the collaborative embedding module is designed to learn a joint latent subspace to capture the coupling of complicated spatiotemporal information in EEG signals. Second, stacked parallel spatial and temporal attention streams are employed to extract the most essential discriminative features and filter out redundant task-irrelevant factors. Finally, the hybrid attention-based feature fusion module is proposed to integrate significant features discovered from the dual-stream structure to take full advantage of the complementarity of the diverse characteristics. Extensive experiments on two publicly available emotion recognition datasets indicate that our proposed approach consistently outperforms state-of-the-art methods.