FPGLNet: A Lightweight Frequency Prior-Gated Network for EEG Emotion Recognition

Although deep learning-based electroencephalography (EEG) emotion recognition methods have achieved promising performance, they generally depend on complex network architectures and high computational costs, which limits their deployment in resource-constrained scenarios. Meanwhile, existing approaches mainly focus on local spatiotemporal feature extraction while insufficiently exploiting global frequency-domain dependencies and dynamic information selection during feature propagation, resulting in inadequate spectral information utilization and redundant feature representations. To address these issues, this paper proposes a Frequency Prior-Gated Lightweight Network (FPGLNet) for EEG emotion recognition. First, a Mixed Aggregation Convolution (MAC) module is introduced at the input stage to jointly encode EEG spatiotemporal characteristics through temporal and spatial convolutions. Subsequently, a Fused Fourier Convolution Mixer (FFCM) module is developed to model global frequency-domain dependencies through Fourier transform and integrate multi-scale depthwise convolutions for collaborative frequency-spatial feature representation. Furthermore, a Prior-Gated Feed Forward Network (PGFN) is proposed to dynamically generate prior-guided gating weights based on feature responses, enabling adaptive selection and recalibration of discriminative features across different brain regions and temporal segments. This mechanism enhances emotion-related representations while suppressing noise and redundant information. Owing to the lightweight architecture design, FPGLNet achieves an effective balance between feature representation capability, model complexity, and computational efficiency. Experimental results demonstrate that FPGLNet achieves classification accuracies of 96.22% and 92.69% on the public SEED-IV and SEED-V datasets, respectively. These results validate the effectiveness of the proposed frequency prior-gated strategy in improving EEG feature representation while maintaining competitive recognition performance and deployment efficiency.