BrainWave Explorer 🧠 ⚡

Exploring the fascinating world of EEG (Electroencephalography) data visualization! This project demonstrates how our brain's electrical activity changes when we simply close our eyes.

The Magic of Alpha Waves ✨

When you close your eyes, something amazing happens in your brain - particularly in the occipital region (the visual processing area). Neurons start synchronizing at around 10Hz, creating what we call "alpha waves". This project visualizes this phenomenon using EEG data!

What's Inside 🎯

• visualize_brain.py: Main Python script for EEG visualization
• S001R01.edf: EEG recording with eyes open
• S001R02.edf: EEG recording with eyes closed
• Sample output showing the dramatic difference!

The Results 📊

What we're seeing here:

1. Raw Signal:

   • Left: Eyes Open (more random)
   • Right: Eyes Closed (more rhythmic)

2. Power Spectrum:

   • Left: No dominant frequency
   • Right: HUGE peak at 10Hz (alpha waves! 🎯)

3. Spectrogram:

   • Left: Scattered activity
   • Right: Strong band around 10Hz (yellow/green)

Requirements 🛠️

import mne
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal

Quick Start 🚀

1. Clone the repository:

git clone https://github.com/inventwithdean/occipital_alpha.git

2. Install requirements:

pip install -r requirements.txt

3. Run the visualization:

python waves.py

Data Source 📚

The EEG data comes from the EEG Motor Movement/Imagery Dataset available on PhysioNet. This project uses:

• Subject: S001
• Recordings:
  • R01 (eyes open)
  • R02 (eyes closed)

Understanding the Code 🤓

The code uses:

• Fast Fourier Transform (FFT) to break down the signal into frequencies
• Welch's method for power spectrum estimation
• Spectrogram for time-frequency analysis

Key Brain Wave Bands 🌊

Band	Frequency	State
Delta	0.5-4 Hz	Deep Sleep 😴
Theta	4-8 Hz	Drowsiness 🌙
Alpha	8-13 Hz	Relaxed, Eyes Closed 👁️
Beta	13-30 Hz	Alert, Focused 🏃

Want to Contribute? 🤝

Feel free to:

1. Fork the repository
2. Create a feature branch
3. Submit a pull request

License 📜

MIT License - feel free to use this for your own EEG adventures!

Next Steps 🎯

Planning to:

• Add more brain regions
• Implement real-time visualization
• Add machine learning analysis
• Create interactive plots

Acknowledgments 🙏

Special thanks to:

• PhysioNet for the amazing dataset
• MNE Python team for the excellent EEG tools
• The fascinating human brain for being so... fascinating!

Citations 📚

This work utilizes the EEG Motor Movement/Imagery Dataset. If you use this code or data, please cite the following papers:

Original Dataset Publication

@article{schalk2004bci,
 title={BCI2000: A General-Purpose Brain-Computer Interface (BCI) System},
 author={Schalk, G. and McFarland, D.J. and Hinterberger, T. and Birbaumer, N. and Wolpaw, J.R.},
 journal={IEEE Transactions on Biomedical Engineering},
 volume={51},
 number={6},
 pages={1034--1043},
 year={2004},
 publisher={IEEE}
}

PhysioNet Citation

@article{goldberger2000physiobank,
  title={PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals},
  author={Goldberger, A. and Amaral, L. and Glass, L. and Hausdorff, J. and Ivanov, P. C. and Mark, R. and others},
  journal={Circulation},
  volume={101},
  number={23},
  pages={e215--e220},
  year={2000}
}

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