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 Aligner (GCA), 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 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.