Enhancing Mental Health Diagnostics using VR/AR

Current Students: Keerthana Srinivasan, Sam Rahimzadeh Holagh, Lavanya Nidamanuri, Johannas Meela Katikala

Status: Current

This project leverages state-of-the-art neurotechnology to better understand and mitigate the impact of stress, anxiety, and depression. The integration of EEG data from Galea and EMOTIV HMDs will facilitate richer insights and potentially transform mental health care through personalized, real-time interventions. The goal is to develop real-time, personalized interventions for managing stress, anxiety, and depression. Leveraging neurotechnology and machine learning, the study aims to map neural activity into emotional and cognitive states, thereby facilitating responsive mental health support systems.

Mental health disorders such as stress, anxiety, and depression are among the leading causes of disability worldwide. Wearable EEG devices offer non-invasive ways to monitor brain activity in real-time, opening pathways for early detection and intervention. Galea (integrated with VR/AR) and EMOTIV (portable, research-grade EEG) present unique opportunities to capture nuanced cognitive-affective data in immersive environments. Combining these tools could provide rich datasets for understanding and managing mental states more effectively.

Expected Outcomes:

A working prototypes system that uses Galea HMD, Emot IV as well as immersive and non-immersive environments to assess and manage mental health states in real-time.
Dataset of labeled multimodal physiological signals related to mental health.
Identification of EEG markers associated with stress, anxiety, and depression.
Comparative insights into signal quality and usability of Galea vs EMOTIV.
Predictive models capable of real-time emotional state classification.
Recommendations for personalized interventions (e.g., neurofeedback, mindfulness prompts).


Galea: Biosensing Headset	EEG Signal Data Analysis (Galea)	EEG Signal to map stress, anxiety, depression

EMOTIV Head set: brain-computer interfaces (BCIs) and electroencephalography (EEG) technology	EMOTIV Head mounted display using EEG signal to map stress and anxiety	EMOTIV HMD using EEG signal to map stress and anxiety

EMOT IV HMD in Unity 3D	Mental Health with ConvAI and VR	AI Assistant using ConvAI
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Publications

Summitt, A., Bhatt, B.J., Sharma, S.,"Emergency Assistive Mobile Application with Digital Twin for Real-Time 3D Navigation in Indoor Environments", Proceedings of the 23rd IEEE/ACIS International Conference on Software Engineering, Management and Applications (SERA 2025), Las Vegas, USA, May 29-31, 2025.