Brain-Computer Interface (BCI) Simulation

A simplified Brain-Computer Interface (BCI) simulation built using Python.

This project demonstrates how brain signals can be simulated, analyzed using signal processing techniques, and classified into mental states based on frequency ranges.

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🧠 What This Project Demonstrates

• Brain wave simulation using sine waves
• Realistic noise addition (to mimic EEG interference)
• Fast Fourier Transform (FFT) for frequency analysis
• Dominant frequency detection
• Rule-based mental state classification

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📊 Brain Wave Frequency Ranges

Brain Wave	Frequency Range	Mental State
Alpha	8–13 Hz	Relaxed
Beta	13–30 Hz	Focused

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⚙️ How It Works

1. Generate a simulated brain signal
2. Add noise to represent real-world EEG conditions
3. Convert the signal from time-domain to frequency-domain using FFT
4. Detect the dominant frequency
5. Classify the mental state

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🚀 Installation

Clone the repository:

git clone https://github.com/devzzafk/bci-simulation.git
cd bci-simulation

Install dependencies:

pip install -r requirements.txt

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▶️ Run the Program

python bci_demo.py

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🧪 Demo Instructions

Inside bci_demo.py, modify:

frequency = 10

Change it to:

frequency = 20

• 10 Hz → Alpha (Relaxed)
• 20 Hz → Beta (Focused)

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📌 Note

This is a simplified educational simulation and does not use real EEG hardware.

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📖 Related Concepts

• Signal Processing
• Fast Fourier Transform (FFT)
• EEG Signals
• Brain-Computer Interfaces
• Feature Extraction

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📜 License

This project is open-source and intended for educational purposes.