Related Experiment Video
Updated: May 19, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Combining EEG, event-related potentials, and MRI biomarkers for detection of mild cognitive impairment: A machine
Young Wook Song1, Young Ho Park2, Seunghu Kim1
1Department of Applied Artificial Intelligence, Hanyang University, Ansan, the Republic of Korea.
Objective:
Mild cognitive impairment (MCI) is an intermediary stage between typical cognitive aging and dementia. Identifying reliable biomarkers for early detection of MCI is crucial for slowing disease progression. This study explored multimodal biomarkers associated with amyloid-positive MCI and assessed wearable EEG/ERP and MRI features using machine learning.
Methods:
This study included 70 patients with MCI with amyloid-positive positron emission tomography (PET) scans and 140 cognitively normal (CN) individuals with negative PET scans. Data were collected using prefrontal wearable EEG/ERP and MRI devices. EEG data were obtained in three paradigms: resting-state, sensory-evoked potentials, and selective attention tasks. Machine learning techniques were applied to classify MCI and CN groups using these biomarkers.
Results:
EEG/ERP analysis suggested differences in frontal alpha asymmetry, power ratio, and ERP components between the MCI and CN groups. MRI revealed atrophy of the hippocampus and accumbens along with cortical thinning in specific brain regions. Combining EEG/ERP and MRI features in a machine learning model yielded the highest classification accuracy of 80.90%.
Conclusions:
Prefrontal wearable EEG/ERP and MRI offered complementary candidate biomarkers of MCI in community-dwelling individuals using a machine learning approach.
Significance:
These findings support further validation of EEG/ERP and MRI biomarkers in larger and externally validated cohorts.