Global-regional anti-noise network for cross-subject EEG emotion recognition

Emotion recognition based on electroencephalogram (EEG) signals has gained interest owing to its potential in affective computing. In practice, EEG signal acquisition is highly prone to noise interference from electrooculography (EOG) and electromyography (EMG) signals, severely impacting the cross-subject emotion recognition performance of models in real-world scenarios. Although learning-based denoising approaches have been developed to address this issue, they rely on standalone autoencoders for reconstruction, resulting in weak integration with downstream tasks. This study proposes a novel global-regional anti-noise network (GRANet) that is specifically designed for robust cross-subject emotion recognition from noisy EEG data. During modeling, GRANet adopts a novel noising training strategy and similarity-based noise-robust (NR) loss function. According to the noising training strategy, GRANet can extract anti-noise features from noisy EEG data using the NR loss. Moreover, GRANet comprises a three-dimensional global convolutional neural network (3D-GCNN) and a three-dimensional regional convolutional neural network (3D-RCNN). These networks aim to separately extract global and regional anti-noise features from noisy EEG data by applying the noising training strategy. We validate the effectiveness of GRANet by comparing it with state-of-the-art (SOTA) cross-subject methods on four benchmark datasets (SEED, SEED-IV, SEED-V, and DEAP). Findings demonstrate that GRANet has superior performance on these datasets, particularly improving average accuracy by 20.97% on SEED-V from the best baseline. In a nutshell, GRANet can robustly handle real-world EEG noise challenges while maintaining high recognition accuracy, establishing it as a promising solution for practical emotion recognition applications.