Exploring cognitive workload recognition using CogRepLKNet with EEG-fMRI.

Accurate multimodal Cognitive Workload Recognition (CWR) remains challenging due to the difficulty of modeling cross-modal relationships between Electroencephalography (EEG) and Functional Magnetic Resonance Imaging (fMRI) data. Additionally, the inherent heterogeneity of these physiological signals-each capturing distinct neural characteristics-complicates unified feature extraction. To address this, we propose CogRepLKNet, a universal re-parameterizable large-kernel convolutional neural network (CNN) designed for multimodal EEG-fMRI modeling. CogRepLKNet employs two parallel universal perception branches consisting of stacked large- and small-kernel CNNs and an adaptive gated attention fusion mechanism to jointly capture complementary temporal-spatial dynamics from both modalities. This design enables efficient feature integration with reduced computational complexity and fewer training samples compared to transformer-based approaches. Through input projections, the perception module enables universal feature extraction across various physiological signals without altering its architecture. Experiments on a self-constructed EEG-fMRI dataset demonstrate that CogRepLKNet achieves state-of-the-art performance, while ensuring low training complexity and easy portability. CogRepLKNet holds great potential for advancing multimodal applications in CWR. Our code is available at https://github.com/prestyan/CogRepLKNet.