🧠 Smart EEG Fatigue Detection System

A cutting-edge project combining real-time EEG data streaming, fatigue analysis, and visualization.

---

🚀 Overview

Smart EEG Fatigue Detection System is a modern full-stack application that simulates, streams, and analyzes EEG brainwave data.
It enables live plotting, data logging, and offline playback for performance tracking and fatigue detection — designed for researchers, developers, and students exploring NeuroTech + AI.

---

🧩 Features

• 🔌 Real-time EEG Data Simulation
  Generates synthetic EEG signals using an asynchronous simulator.

• 📡 Live WebSocket Streaming
  Uses FastAPI + WebSockets for real-time bidirectional data exchange.

• 📈 Live Plot Visualization
  Displays continuous brainwave graph updates using Matplotlib.

• 💾 Automatic Data Logging
  Saves EEG samples locally in .csv format for later analysis.

• 🔁 Offline Replay Mode
  Replay and visualize previously recorded EEG sessions anytime.

• ⚡ FastAPI Backend API
  Handles data input/output, model integration, and session management.

• 🧠 Fatigue Detection Logic (extendable)
  Ready to plug in ML/AI models for real fatigue state prediction.

---

🧬 Tech Stack

Layer	Tools & Libraries
Backend	FastAPI, Uvicorn, Pydantic
Realtime	WebSockets
Simulator	Python AsyncIO, Matplotlib
Data Handling	CSV, JSON
Frontend (optional)	React / Streamlit (extendable)
Visualization	Matplotlib, Numpy
Deployment	Docker, GitHub Actions (planned)

---

🧠 System Architecture

flowchart LR
A[EEG Simulator 🧩] -->|WebSocket| B(FastAPI Backend ⚙️)
B --> C[(Database / CSV Logger 💾)]
B --> D[Visualization / Dashboard 📊]
D --> E[User 🧍‍♂️]

Loading