Exploratory Integration of EEG Spectral Features and Gaze Variability for Mild Cognitive Impairment Discrimination

2026-07-31

AI Summary

Early identification of mild cognitive impairment (MCI) is crucial in aging societies, yet unimodal approaches often exhibit limited discriminative power. This study systematically investigates, for the first time, the complementary value of electroencephalographic (EEG) spectral features and eye movement variability in MCI classification. EEG signals were recorded using the 10–20 system, and spectral power features were extracted and refined via LASSO regularization for feature selection, then integrated with eye movement variability metrics. Experimental results demonstrate that raw high-dimensional EEG features yield an AUC of 0.52, which improves to 0.64 after LASSO-based selection; notably, multimodal fusion with eye movement variability further elevates the AUC to 0.78, substantiating the efficacy and added benefit of integrating complementary neurophysiological and oculomotor biomarkers for MCI detection.

Abstract

Early detection of mild cognitive impairment (MCI) is an important challenge in aging societies. Electroencephalography (EEG) and eye-tracking have independently been explored as potential biomarkers; however, their integrative effects remain insufficiently examined. This exploratory study investigated whether combining EEG spectral features with gaze variability may provide complementary information for MCI discrimination. EEG signals were recorded using the 10--20 system, and spectral power features were extracted. We compared three models: (a) high-dimensional EEG features, (b) L1-regularized feature selection (LASSO), and (c) integration of the selected EEG features with gaze variability. Performance was evaluated using leave-one-out cross-validation and area under the ROC curve (AUC). Model (a) yielded limited discrimination (AUC = 0.52). Feature selection increased AUC (0.64), and additional integration of gaze variability further increased AUC (0.78). These preliminary findings suggest potential complementarity between neural and behavioral variability measures.

Problem

• mild cognitive impairment
• EEG spectral features
• gaze variability
• early detection
• biomarkers

Innovation

• EEG spectral features
• gaze variability
• mild cognitive impairment
• multimodal integration
• LASSO feature selection