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.

Traditional authentication methods, such as biometrics and passwords, cannot guarantee security once the authenticated user changes after login. Periodic re-authentication at short intervals could preserve security but would severely degrade usability. To balance security and usability, we propose an implicit and continuous identification method based on eye-tracking data. Our approach compares a saliency map personalized for the legitimate user with the gaze heatmap of the current user, and identifies the current user as legitimate if the two maps are similar. To demonstrate our approach, we first develop and verify that a personalized saliency map well expresses a legitimate user’s gaze behavior. With one example result of the user study, we then illustrated the potential application of our identification as a second-factor authentication and as an unobtrusive re-authentication suggestion.

We release an eye-tracking dataset recorded with a Tobii Pro Fusion eye tracker (120 Hz) across six tasks: (1) repeated free viewing of 100 images over three sessions (300 presentations per participant), (2) gaze-pattern authentication, (3) numeric password entry, and (4-6) three 10-minute naturalistic screen activities (shopping, news browsing, and video viewing). Our key design choice is to present the same 100 images three times to obtain stable, participant-level average gaze behavior; the image order is deterministically randomized with a fixed seed such that all participants view the same sequence. Due to copyright restrictions, we provide only the 80 copyright-free natural images collected from the internet as stimuli. The dataset is released under the CC BY 4.0 license. We do not include baseline models or benchmark evaluations, allowing researchers to apply their own post-processing and modeling approaches to the unconstrained, raw tabular data.

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.

In recent studies on gaze tracking systems using 3D model-based methods, the optical axis of the eye was estimated without user calibration. The remaining challenge in achieving implicit user calibration is estimating the difference between the optical and visual axes of the eye (angle κ). In this study, we propose two methods that improve the implicit user calibration method using saliency maps, focusing on eye movement to reduce calculation costs while maintaining accuracy.

A 3D gaze-tracking method that uses two cameras and two light sources can measure the optical axis of the eye without user calibration. The visual axis of the eye (line of sight) is estimated by conducting a single-point user calibration. This single-point user calibration estimates the angle k that is offset between the optical and visual axes of the eye, which is a user-dependent parameter. We have proposed an implicit user calibration method for gaze-tracking systems using a saliency map around the optical axis of the eye. We assume that the peak of the average of the saliency maps indicates the visual axis of the eye in the eye coordinate system. We used both-eye restrictions effectively. The experimental result shows that the proposed system could estimate angle k without explicit personal calibration.