Dr. DrEEG

Dr. DrEEG is a web app that combines neuroscience, technology, and music to help users connect with their emotions and others. The app uses the Muse-S headband to detect brainwave activity (EEG) and analyze the user's emotional state in real-time. Based on this analysis, the app recommends karaoke songs that either match or uplift the user's mood.

The app was built using Next.js for the frontend and Python with FastAPI for the backend, which integrates machine learning models to analyze emotional states. The Muse-S headband is used to collect EEG data, which is then processed to generate song recommendations.

While developing Dr. DrEEG, the team faced several challenges, such as pivoting ideas within the limited 64-hour timeframe and dealing with the technical difficulties of using the Muse-S headband for emotion detection. Although there was not enough time to train a custom machine learning model, we successfully utilized an existing model to implement emotion detection.

We are proud of our AI-powered emotion detection, user-friendly interface, and the innovative integration of neuroscience, AI, and entertainment. Moving forward, we plan to enhance the emotion detection system by building a custom AI model, add features more karaoke features like duet mode and social sharing, and introduce a biofeedback mode where users can regulate their emotions with tailored music to calm or energize them.

Built With

• fastapi
• geniusapi
• muse
• muses
• next.js
• react
• scikit-learn
• spotifyapi
• tailwind
• tensorflow
• three.js
• typescript
• websockets