MindMapp: Revolutionizing Depression Diagnosis and Management Through EEG and AI Chatbot Integration
Rhea Shishodia1, Tina Singh1, Shannon Giliberto1, Sadeepa Bulathsinhala2, Jonathan Terry1
1CHSUCOM, 2St George's University School of Medicine
Objective:
- To propose a novel approach to understanding and managing depression using a combination of subjective and objective data from an AI chatbot and EEG sensors.
Background:
Artificial intelligence (AI) and machine learning have transformed mental health, particularly through mobile applications (apps). Depressive disorders are the leading mental health disorders affecting nearly 300 million people worldwide and correspond with specific electroencephalographic (EEG) signatures; yet, consumer-level mental health apps don’t have the ability to provide neurofeedback. To address this gap, our research project focuses on developing a mental health app with EEG compatibility.
Design/Methods:
A systematic review of PubMed articles was conducted to explore the potential role of neurofeedback in diagnosing and treating depression. Consumer reviews of successful mental health apps were analyzed to model our app based on their features. A comparison with existing apps (Youper, Woebot, MindDoc, etc) is presented to showcase the unique benefits of our app.
Results:
Based on combined results from published studies, it is evident that “the absolute power of the theta wave” is an essential EEG finding integral for neurofeedback potential. Current mental health apps rely solely on subjective data via an AI chatbot and/or biofeedback via phone sensors, and thus provide limited potential for accurate diagnosis and treatment for depressive mood disorders. Beyond existing application limitations, MindMapp aims to utilize objective EEG and sensory data in addition to subjective emotional data from an AI chatbot to provide a more accurate diagnosis and more efficient coping methods tailored to each individual.
Conclusions:
Depression is a significant global health concern, and MindMapp aims to offer accessible means for accurate diagnosis and management from home. Future research should address concerns related to clinical data misuse, ethics, cost, and usability of mobile EEG technology. Expansion to other mental health disorders is also planned once the depression algorithm is established.