Adaptive spatiotemporal encoding network for cognitive assessment using resting state EEG

Cognitive impairment, marked by neurodegenerative damage, leads to diminished cognitive function decline. Accurate cognitive assessment is crucial for early detection and progress evaluation, yet current methods in clinical practice lack objectivity, precision, and convenience. This study included 743 participants, including healthy individuals, mild cognitive impairment (MCI), and dementia patients, with collected resting-state EEG data and cognitive scale scores. An adaptive spatiotemporal encoding framework was developed based on resting-state EEG, achieving an MAE of 3.12% (95% CI: 2.9034, 3.3975) in testing (sensitivity: 0.97, 95% CI: 0.779,1; specificity: 0.97, 95% CI: 0.779,1). The model's effectiveness was also validated on the neurofeedback (sensitivity: 0.867, 95% CI: 0.621, 0.963; specificity: 1, 95% CI: 0.439, 1.0) and TMS datasets (sensitivity: 0.833, 95% CI: 0.608, 0.942), which effectively reflect the participants' cognitive changes. The model effectively extracted repetitive spatiotemporal patterns from resting-state EEG, aiding in cognitive disease diagnosis and assessment in various scenarios.