Spatial Cognitive Electroencephalogram Network Topological Features Extraction Based on Cross Fuzzy Entropy Network Graph

Spatial cognition, a critical component of human cognitive function, can be enhanced through targeted training, such as virtual reality (VR)-based interventions. Recent advances in electroencephalography (EEG)-based functional connectivity analysis have highlighted the importance of network topology features for understanding cognitive processes. In this paper, a framework based on a cross fuzzy entropy network graph (CFENG) is proposed to extract spatial cognitive EEG network topological features. This framework involves calculating the similarity and symmetry between EEG channels using cross fuzzy entropy, constructing weighted directed network graphs, transforming one-dimensional EEG signals into two-dimensional brain functional connectivity networks, and extracting both local and global topological features. The model’s performance is evaluated and interpreted using an XGBoost classifier. Experiments on an EEG dataset from group spatial cognitive training validated the CFENG model. In the Gamma band, the CFENG achieved 97.82% classification accuracy, outperforming existing methods. Notably, the asymmetrically distributed EEG channels Fp1, P8, and Cz contributed most to spatial cognitive signal classification. An analysis after 28 days of training revealed that specific VR games enhanced functional centrality in spatial cognition-related brain regions, reduced information flow path length, and altered information flow symmetry. These findings support the feasibility of VR-based spatial cognitive training from a brain functional connectivity perspective.