MsGCN: a multi-stream graph convolutional network for multiband PLV graph fusion in EEG-based biometric identification.

Introduction: EEG-based biometric identification has attracted extensive attention due to its high security and uniqueness. Functional connectivity features derived from EEG exhibit strong individual specificity, yet existing methods do not fully leverage the complementary identity information contained in multiband functional connectivity features. Methods: This study proposes a multi-stream graph convolutional network (MsGCN) for EEG-based biometric identification by fusing graph representations derived from multiband phase-locking value (PLV) matrices. The model processes PLV matrices from multiple frequency bands through parallel GCN branches and performs end-to-end identification using fully connected layers. Experiments on the public PhysioNet Motor Movement/Imagery dataset evaluated the method under non-preprocessed conditions, cross-task settings, channel reduction, and different graph binarization thresholds. Results: MsGCN achieved 99.50% accuracy on preprocessed data and 98.12% on non-preprocessed data, showing numerically higher accuracy than the selected CNN and GCN baselines under the unified protocol. The model also showed improved robustness in cross-task identification, reduced-channel settings, and across a wide range of thresholds. Discussion: These results suggest that multiband PLV graph fusion can improve robustness to preprocessing conditions, task variation, channel reduction, and threshold selection under the evaluated dataset and experimental settings.