Ez-G Creator

Automated EEG Electrode placement

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Inspiration

EEG helmets offer a powerful, non-invasive way to measure brain activity, making them invaluable for research, clinical, and practical applications. In neuroscience, they help study cognition, sleep, and mental disorders. Clinically, they aid in diagnosing epilepsy, monitoring anesthesia, and developing brain-computer interfaces. On a practical level, they enable neurofeedback for mental well-being, hands-free control in assistive technology, and even brain-driven gaming. Their portability and ease of use are expanding possibilities for real-time brain monitoring outside traditional labs.

EEG Helmets are the future of non invasive solutions to develop practical solutions for individuals with various disabilities.

However, EEG helmets are notoriously difficult to create/use because electrode placement changes with head shape and size. Even small misalignments can affect accuracy, requiring manual adjustments and calibration for reliable data.

What it does

Our Ai implementation will utilize a picture of a users face and corresponding 3d model, where it will track the users facial landmarks and develop the optimal location for electrodes. Allowing quick, efficient, and simple methodology to create an EEG helmet for anybody.

How we built it

The front-end was developed through React.js. The image processing is done by a 2d/3d face alignment model, and electrode placement is done through various python backend scripts. The entire backend is tied together using a FlaskAPI, allowing seamless interactions between the front and back-end.

What we learned

We learned how to use use a 2D face alignment model to determine facial features in 3D space. We learned how to implement FlaskAPI for front and back-end as well as Gemini API to design personalized electrodes.

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