Geometric Invariant Moment-Based Feature Extraction for Cognitive Load Monitoring

Abstract

EEG-based cognitive workload assessment has attracted considerable attention in fields such as brain–computer interfaces and human–computer interaction due to its high temporal resolution and non-invasive nature. However, existing studies mainly focus on time-domain and frequency-domain features, while effective modeling of EEG spatial topological structures remains insufficient and is highly susceptible to electrode displacement and inter-subject variability. To address these issues, an EEG spatial modeling method based on Hu invariant moments is proposed for cognitive workload recognition. First, multi-band power spectral features are mapped into two-dimensional scalp topographic maps. Subsequently, Hu invariant moments with translation, rotation, and scale invariance are extracted to characterize the global spatial distribution patterns of EEG signals. The resulting geometric-invariant representation is incorporated into the original multi-domain representation of DHPL-Net, providing an explicit spatial-topological complement to conventional spectral and statistical features without modifying the network architecture. Experiments conducted on the public STEW dataset demonstrate that the incorporation of Hu invariant moments improves both accuracy and F1-score compared with the baseline method, while multi-seed experimental results further verify the robustness of the proposed approach.