BioAmp-Python-Notebooks

BioAmp-Python-Notebooks is a curated collection of beginner-friendly Python notebooks designed to help users understand and work with bio-potential signals using hardware like BioAmp boards and analysis tools such as NeuroKit2.

Currently, this repository features a complete ECG (Electrocardiogram) Signal Processing and Analysis Notebook, ideal for students, researchers, and hobbyists who want to learn the step-by-step workflow of acquiring, processing, and analyzing ECG data in real-time.

📌 Features of the ECG Notebook

This notebook is structured to guide you through every stage of ECG signal analysis:

1. Library Imports

Start by importing all necessary Python libraries such as numpy, matplotlib, scipy, pandas, and neurokit2.

2. Initialize Chords_USB Client

This cell checks for connected compatible hardware, initializes the client, and starts real-time data streaming from the ECG hardware.

3. Real-time ECG Data Acquisition

Collect ECG data in real-time. This cell sets up a live plot window that updates continuously as data is received for a specified time duration.

4. Signal Processing

Apply a notch filter (to remove powerline interference) and a low-pass filter to clean the raw ECG signal. The processed signals are then saved to a CSV file for further analysis. Visualizations are also provided for comparison.

5. ECG Analysis Pipeline using NeuroKit2

Use the neurokit2 library to extract meaningful features from the ECG signal, including:

• PQRST Complex Analysis: Detect and label P, QRS, and T waves.
• ECG Beat Morphology: Align beats to R-peaks, visualize individual beats, and compute an average beat morphology.

6. Stop Streaming

Send a STOP command to terminate the data acquisition process safely.

🎯 Who Is This For?

• 🧑‍🎓 Students looking for a hands-on introduction to bio-potential signal processing.
• 🧪 Researchers interested in analyzing ECG data using Python.
• 💡 Developers building health-related apps or tools.
• ❤️ Hobbyists experimenting with bio-potential signals and DIY health tech.

🚀 Getting Started

1. Clone this repository

git clone https://github.com/upsidedownlabs/BioAmp-Python-Notebooks.git

2. Install required packages

pip install -r requirements.txt

3. Run the ECG Notebook

   • Open the .ipynb file in Jupyter Notebook or JupyterLab.
   • Follow each cell in sequence.

📌 Upcoming Notebooks

The repo will gradually include:

• EMG (Electromyography) Signal Analysis
• EEG (Electroencephalogram) Feature Extraction
• EOG (Electrooculogram) Blink Detection

Stay tuned!

🤝 Contributing

Contributions, issues, and suggestions are welcome! If you have your own notebooks or improvements, feel free to submit a pull request.