探索性整合脑电（EEG）频谱特征与注视变异性以用于轻度认知障碍（MCI）的判别
Exploratory Integration of EEG Spectral Features and Gaze Variability for Mild Cognitive Impairment Discrimination
AI总结：
本研究探索整合EEG频谱特征与注视变异性，通过三种模型对比发现，整合后模型AUC达0.78，可提升轻度认知障碍判别性能，揭示神经与行为测量的互补性。
AI中文摘要：
轻度认知障碍（MCI）的早期检测是老龄化社会中的重要挑战。脑电（EEG）和眼动追踪已被分别研究作为潜在生物标志物，但它们的整合效应仍未得到充分检验。本探索性研究探究将EEG频谱特征与注视变异性结合是否能为MCI判别提供互补信息。采用10-20系统记录EEG信号并提取频谱功率特征，对比三种模型：（a）高维EEG特征；（b）L1正则化特征选择（LASSO）；（c）所选EEG特征与注视变异性的整合。采用留一交叉验证和ROC曲线下面积（AUC）评估性能，模型（a）判别能力有限（AUC=0.52），特征选择使AUC提升至0.64，进一步整合注视变异性后AUC增至0.78。这些初步发现表明神经与行为变异性测量间存在潜在互补性。
英文摘要：
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.
