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.

The increasing prevalence of Alzheimer’s disease (AD) highlights the need for efficient and early detection. However, traditional cognitive assessments, such as the Mini-Mental State Examination (MMSE), which are administered in person, may not capture subtle early stage impairments. Recent studies have suggested that eye movements can serve as a biomarker for AD. In this study, we examined the use of dynamic time warping (DTW) to analyze smooth-pursuit eye movements to detect cognitive decline in 24 elderly participants, of whom 18 had valid eye tracking data for analysis. DTW was chosen because of its ability to capture the temporal alignment between the target and gaze trajectories. DTW demonstrated a stronger correlation with the MMSE scores (R = -0.782 and R 2 = 0.612) than with the conventional Pearson correlation coefficient (R = 0.456 and R 2 = 0.208). These findings highlight the potential of DTW-based analyses to complement existing cognitive screening tools in clinical settings. Future research should expand the participant pool and explore different smooth-pursuit paradigms to improve the predictive accuracy.

This poster presents a study on detecting Alzheimer’s disease (AD) using deep learning from gaze data. In this study, we modify an existing pre-trained deep neural network model, gazeNet, for transfer learning. The results suggest the possibility of applying this method to mild cognitive impairment screening tests.

Early detection of Alzheimer’s disease (AD) is important but difficult. Screening for AD using neuropsychological tests such as mini-mental state examination (MMSE) is time-consuming and burdensome for patients. Recently, several methods have been reported for detecting AD based on eye movements. However, analyzing eye movements requires considerable effort. Although machine learning from eye movement data is a strong candidate for labor-saving, it requires large datasets. In this study, we modify an existing pre-trained deep neural network model, gazeNet, for transfer learning. For evaluation, we exclusively used data from one participant and fine-tuned the model using data from all the remaining participants. We repeated this procedure separately for each of the 14 participants. The results of eye movement during the antisaccade task were not satisfactory for the discrimination of AD, and detailed analysis suggested that the data might potentially have a correlation with MMSE scores in the mild cognitive impairment range.