The purpose of this project is to revolutionize music recommendation systems by integrating neuroscience and artificial intelligence to create playlists that respond to real-time emotional states. Unlike traditional recommendation models that rely on listening history and genre preferences, Mind Beats leverages Brain-Computer Interface (BCI) technology to analyze EEG signals and determine a user's mood with high accuracy. By doing so, it aims to break the restrictive feedback loops of conventional algorithms and instead offer a deeply personalized listening experience that enhances positive emotions and alleviates negative ones. This innovation not only redefines how music is consumed but also explores its potential as a tool for emotional well-being, paving the way for future advancements in neuro-adaptive technology.
Streamlit, OpenAI, Muse
One of the biggest challenges was accurately interpreting EEG signals and mapping them to emotional states given our lack of experience with neuroscience. We spent a lot of time reading and researching to accurately understand EEG Signals and overcome this challenge.
OpenAI, Muse
git clone https://github.com/pranayjoshi/mind_music.gitcd mind_musicpip install -r requirements.txtstreamlit run emotionprediction.py
git add .git commit -m "<your-text>"git push
1:- Pranay Joshi
2:- Gaurav Shrivastava
3:- Kavya Gupta
4:- Priyanshu Sethi