Early Detection of Patients With Mild Cognitive Impairment Through EEG-SSVEP-Based Machine Learning Model

Mild cognitive impairment (MCI) is a transitional stage from normal aging to Alzheimer's disease (AD). Early detection of MCI is essential, as it offers a last opportunity for interventions to slow or prevent progression to AD. However, identifying effective biomarkers for screening remains challenging. While declines in perception and action often precede visible neurodegenerative changes, studies on visual pathway biomarkers for MCI detection are limited. In this study, we focused on electroencephalography with steady-state visual evoked potentials (EEG-SSVEP), known for its high-resolution, real-time monitoring of brain response to flicker stimulation, as a promising method for early MCI identification. We collected EEG-SSVEP data from 24 healthy controls and 25 MCI patients, extracting 166 EEG-SSVEP biomarkers, including lobe power ratio, lobe connectivity ratio, and band connectivity ratio, to assess the visual pathway's dorsal and ventral streams related to cognitive decline. By employing a biomarker selection method, we identified six key EEG-SSVEP biomarkers as the most relevant for distinguishing between healthy controls and MCI patients. Subsequently, these six biomarkers were utilized to train a support vector machine for early detection of MCI. The results showed an accuracy rate of 95.69%, a sensitivity of 92.28%, and a specificity of 95.58%. This study offers valuable insights into enhancing the early detection of MCI by leveraging EEG-SSVEP data and machine learning to assess cognitive decline within the dorsal and ventral streams of the brain.