Complex-valued brain networks for neurodegenerative disease diagnosis via component-aware feature fusion

Introduction Recent advancements in brain network analysis have greatly improved the diagnosis of neurodegenerative diseases. However, most existing studies rely on single-frequency EEG representations and overlook the joint modeling of real and imaginary connectivity in the frequency domain. Methods To address this limitation, we propose a novel complex-valued brain network framework for diagnosis through component-aware feature fusion. EEG signals are first transformed into complex-valued representations using frequency-domain filtering. A Complex-valued Brain Network Construction (CBNC) module with multi-scale real and imaginary convolutions is then employed to capture dynamic inter-channel interactions. Finally, a Component-Aware Feature Fusion (CAFF) mechanism integrates multicomponent features by modeling cross-component semantic consistency, leading to more expressive and physiologically meaningful brain networks. Results Extensive experiments on two benchmark datasets show that the proposed method achieves an accuracy of 91.59% for mild cognitive impairment detection and 99.99% for stroke detection, consistently surpassing state-of-the-art methods in both accuracy and robustness. Discussion These results demonstrate that integrating real and imaginary connectivity with component-aware feature fusion offers a more effective and physiologically grounded representation of brain networks. The proposed framework provides a promising direction for improving the diagnosis of neurodegenerative diseases.