A Spatial-Spectral-Temporal Representation Method Based on Riemannian Manifold for EEG Individual Identification

With the advancement of neuroscience and computer science, electroencephalography (EEG) has drawn increasing attention as a promising modality for biometric identification, owing to its universality, permanence, and security. However, existing studies have pointed out that maintaining stable and temporally robust inter-individual features remains a major challenge in EEG-based identification. Therefore, developing effective methods for cross-time EEG-based identity recognition is essential for achieving reliable and practical biometric systems. In this study, we propose a novel EEG-based identification framework grounded in symmetric positive definite (SPD) manifolds. Specifically, we utilize the spatial covariance matrices of EEG signals to represent individual differences and introduce an enhanced feature extraction method (E-SPD-M) that simultaneously captures temporal, spatial, and spectral characteristics. These matrices are embedded into the Riemannian manifold to construct a discriminative representation space. For each subject, we build a personalized classification model and integrate their outputs to achieve accurate identification. Furthermore, we construct a comprehensive multi-task, cross-time EEG dataset and validate our approach on both our dataset and a publicly available longitudinal EEG dataset (M3CV). Experimental results demonstrate that our method achieves superior cross-time identification performance. Overall, this work offers a novel pathway for improving EEG-based biometric algorithms and extending the application of Riemannian geometry in the field.