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HackMIT 2025 TempoRoll

Project Overview

TempoRoll is an EEG-based therapeutic music system that analyzes brainwave patterns in real-time to generate or select personalized music for emotional regulation and mental wellness.

System Architecture

Data Flow:

  • EEG Device → Frontend → Backend → LLM Analysis → Music Generation/Selection → Audio Playback

Processing Pipeline:

  • EEG brainwave data → ML emotion classification → LLM therapeutic analysis → Music recommendation → Audio output

Folder Structure

/backend/

Core server application that processes EEG data and handles music generation/selection:

  • Socket server for receiving EEG data from frontend
  • ML-based emotion inference from brainwave patterns
  • LLM integration for therapeutic music recommendations
  • Music generation via Suno API or selection from existing files
  • Audio playback and session management

/frontend/

Nuxt.js web application providing the user interface:

  • Dashboard for real-time EEG visualization
  • User session management and controls
  • WebSocket client for backend communication
  • Responsive UI for monitoring brainwave patterns and music playback

/ml/

Machine learning components for emotion classification:

  • EEG data preprocessing and feature extraction
  • Emotion classification model training and inference
  • Model persistence and loading utilities

/models/

Trained machine learning models:

  • Brainwave emotion classification models
  • Label encoders and preprocessing artifacts

/neurosky-comms/

NeuroSky EEG device communication:

  • Device connection and data reading
  • Real-time brainwave data streaming
  • Socket communication with backend server

/suno/

Music generation API integration:

  • Suno API client for generating therapeutic music
  • Audio file management and download utilities

Key Features

  • Real-time EEG Analysis: Continuous monitoring of brainwave patterns
  • Emotion Classification: ML-powered emotion inference with 85% accuracy
  • Therapeutic Music: LLM-generated personalized music recommendations
  • Dual Mode Operation: Real-time music generation or selection from existing library
  • Session Management: User profile and session data persistence

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