Recent advancements in cognitive impairment research have led to significant progress. Electroencephalography (EEG)-based cognitive state identification can detect early cognitive decline in the elderly, providing a critical window for intervention. As cognitive function worsens, neural activity patterns in the brain also change. Functional connectivity between brain regions, a key indicator of synchronized neural activity, is widely used to reveal brain network characteristics under different cognitive states. In this study, we introduce a novel framework, PowerSyncNet, based on functional connectivity to identify cognitive states. PowerSyncNet mainly consists of three modules. The Channel-Pair Feature Sequences Builder extracts features that characterize functional connectivity across different frequency bands. The Encoder4Band module captures temporal-frequency representations that reflect cognitive states and combines cross-band information to improve feature clarity. The Classifier then determines the corresponding cognitive states. We tested PowerSyncNet on the publicly available Chung-Ang University Hospital EEG (CAUEEG) dataset and our own collected Emotion and Cognition EEG (ECED) dataset. Results show that PowerSyncNet has superior cognitive identification capabilities compared with existing deep learning frameworks, facilitating early assessment and timely intervention for patients with cognitive impairment.
Li et al. (Mon,) studied this question.