A brain-computer interface that uses Muse 2 consumer EEG hardware to detect P300 attention-evoked potentials, combined with Gemini-powered phrase prediction, to enable rapid assistive communication.

1. Muse 2 streams 4-channel EEG (TP9, AF7, AF8, TP10) via Lab Streaming Layer
2. A real-time signal pipeline applies bandpass filtering and stimulus-locked epoching
3. An LDA classifier detects P300 responses to identify which phrase the user is focusing on
4. Gemini Flash generates 6 contextual phrase suggestions based on conversation history
5. A Pygame window flashes phrases in sequence (oddball paradigm)
6. Blink to confirm a selected phrase, jaw clench to delete/undo

Muse 2 → muselsl → LSL → FastAPI Backend → Pygame Stimulus Window
                              ↕ WebSocket        ↕ IPC
                         React Frontend    Flash + Markers

• FastAPI backend — central hub (EEG pipeline, Redis, LDA classifier, Gemini API)
• Pygame window — precise stimulus flashing for P300 oddball paradigm
• React frontend — dashboard and controls (candy-sky themed)

• Python 3.10+
• Node.js 18+
• Redis server
• Muse 2 headband (or use simulated mode)
• Gemini API key

cd backend
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

cp .env.example .env
# Edit .env with your GEMINI_API_KEY

cd frontend
npm install

cp .env.example .env
# Edit .env with your GEMINI_API_KEY (for TTS)

Option A — Simulated EEG (no Muse 2 needed):

# Terminal 1: Start Redis
redis-server

# Terminal 2: Start backend (simulated EEG)
cd backend
source .venv/bin/activate
SIMULATE_EEG=true python main.py

# Terminal 3: Start frontend
cd frontend
npm run dev

• Backend API: http://localhost:8000 (Swagger docs at /docs)
• Frontend: http://localhost:3000
• Pygame stimulus window opens automatically

If no saved model exists, click "Calibrate" in the frontend. Follow the Pygame prompts (~2-3 minutes). The model saves to backend/models/p300_lda.joblib and reloads automatically on restart.

Backend: FastAPI, muselsl, pylsl, Redis, SciPy, scikit-learn, Pygame, Gemini API Frontend: React 19, Vite, TypeScript, Tailwind CSS, Recharts, Framer Motion, Gemini TTS