Few-Shot Cross-Subject EEG Cognitive Load Assessment Based on Global Cross-Attention Domain Adaptation

Cognitive load (CL) assessment is crucial for optimizing human–machine interaction (HMI), enabling dynamic task allocation and efficient coordination between human and machine resources to enhance adaptability and performance. Electroencephalography (EEG), as a key physiological signal captured via wearable electrodes, provides objective evidence for real-time and accurate CL monitoring. However, cross-subject variability and the difficulty of collecting labeled EEG data pose significant challenges to reliable CL assessment. Existing methods are limited by their reliance on large-scale labeled data and their tendency to compromise shallow features during domain alignment. To address these limitations, we propose the global cross-attention (GCA) aligner, a novel domain adaptation framework that improves cross-subject EEG-based CL assessment using only 1% of labeled target data. GCA employs a cross-attention mechanism to preserve crucial shallow features while aligning conditional distributions across domains. Combined with a source domain selector and adversarial training, it achieves state-of-the-art (SOTA) accuracy (78.76%–98.20%) on five public datasets and our self-collected dataset, outperforming baselines by over 3%. This work advances adaptive HMI systems driven by wearable sensors and promotes few-shot learning in EEG-based brain–computer interfaces (BCIs). Code will be available after acceptation.