Authors: Siddharth Mahankal, Suyesh Shinde, Akash Unhale, Dr. Jagannath Nalavde Arun, Hirmukhe
Abstract: Mental health issues are becoming increasingly common among individuals over the age of 16, with academic, social, and career-related stress being the main reasons. Traditional mental health assessment methods, such as surveys or counseling, are often affected by personal biases or insufficient information. With this in mind, our project presents a system that collects and analyzes EEG (electroencephalogram) brain wave graphs across different mental states — such as awake, drowsy, and deep sleep. Users upload a multi-page PDF report containing the EEG graph to the system. This PDF is then separated into pages, analyzed with the help of a trained machine learning model, and the percentage probability of the user’s mental state being normal or abnormal is displayed. The system is trained on a dataset of EEG graphs classified by experts. Importantly, this project is not intended to be a substitute for medical diagnosis or professional mental health services. Instead, the project seeks to raise awareness and act as a link between individuals and mental health professionals, especially for those who may not recognize the need for help. The ultimate goal is to make early mental health screening in educational institutions more accessible, data-based, and stigma-free.
DOI: http://doi.org/
