Enhancing Autism Diagnosis: Integrating EEG with Eye-Gaze Metrics in Virtual Reality Environments

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social interaction, communication, and behavior. Current diagnostic methods often rely on behavioral assessments, which can be subjective and may lead to delayed or inaccurate diagnoses. This paper explores the integration of electroencephalography (EEG) with eye-gaze metrics within virtual reality (VR) environments as an innovative approach to enhance the diagnosis of ASD. By leveraging the immersive and controlled nature of VR, we aim to elicit naturalistic responses that are quantifiable and replicable, facilitating a more objective diagnostic process.

Our approach involves the simultaneous collection and analysis of EEG data and eye-gaze patterns as participants engage in carefully designed VR scenarios. EEG provides insights into neural activity, offering potential biomarkers for ASD, while eye-tracking metrics reveal atypical gaze behaviors associated with the disorder. The combination of these modalities within a VR framework enables the capture of dynamic interactions, allowing for the assessment of social and cognitive functions in real-time.

Initial findings suggest that individuals with ASD exhibit distinct EEG signatures and eye-gaze patterns compared to neurotypical controls. Specifically, deviations in theta and gamma band power, coupled with reduced gaze fixation on socially relevant stimuli, were observed. These indicators, when analyzed using machine learning algorithms, demonstrate high potential for distinguishing ASD from typical developmental trajectories with improved accuracy.

This study underscores the potential of integrating EEG and eye-gaze metrics in VR as a multifaceted diagnostic tool for ASD. By providing a richer, more objective dataset, this approach holds promise for early diagnosis, which is crucial for timely intervention. Future work will focus on refining these techniques and validating them across diverse populations to ensure their robustness and applicability in clinical settings.