Python Intan

python-intan is a comprehensive Python package for working with Intan Technologies RHX systems and electrophysiology data. From file loading to real-time streaming, signal processing to machine learning, hardware integration to GUI applications—everything you need for EMG/neural data analysis in one package.

✨ Key Features

📁 File I/O: Load .rhd, .rhs, .dat, .csv, and .npz files with ease
🔴 Real-time Streaming: TCP interface for live data acquisition from RHX devices
🎛️ Signal Processing: Filtering, normalization, RMS, feature extraction
🤖 Machine Learning: Complete gesture classification pipeline with TensorFlow
📊 Visualization: Waterfall plots, real-time plotting, GUI applications
🔌 Hardware Integration: LSL support, Raspberry Pi Pico, robotic control
GUI Applications: EMG viewer, trial selector, gesture pipeline interface (PyQt5 preferred; Tkinter fallback)
🚀 Performance: GPU acceleration, optimized for real-time applications

📚 Quick Links

Documentation - Full guides and API reference
Examples - 60+ code examples
FAQ - Frequently asked questions
Contributing - How to contribute
Changelog - Version history

📦 Installation

From PyPI (Recommended)

pip install python-intan

From Source (Latest Features)

git clone https://github.com/Neuro-Mechatronics-Interfaces/python-intan.git
cd python-intan
pip install -e .

With Virtual Environment

# Using conda
conda create -n intan python=3.10
conda activate intan
pip install python-intan

# Or using venv
python -m venv intan
source intan/bin/activate  # Windows: intan\Scripts\activate
pip install python-intan

Optional Features

GPU Support - For faster machine learning training: follow the official TensorFlow GPU installation guide for the correct pip wheel and system CUDA/CuDNN versions:

https://www.tensorflow.org/install

As an example, to install the CPU-only TensorFlow wheel pinned to the project version:

pip install tensorflow==2.19.0

Video Processing - For hand landmark tracking and finger kinematics:

pip install 'python-intan[video]'

Includes: opencv-python, mediapipe, and handtrack package.

GUI Applications - The GUI apps (EMG viewer, trial selector, gesture pipeline) are optional. To install GUI dependencies:

pip install 'python-intan[gui]'

Includes: PyQt5 and pyqtgraph.

🚀 Getting Started

Load and Visualize EMG Data

import intan

# Load .rhd file (opens file picker)
result = intan.io.load_rhd_file()

# Or specify path directly
result = intan.io.load_rhd_file('path/to/file.rhd')

# Access data
emg_data = result['amplifier_data']  # Shape: (channels, samples)
fs = result['frequency_parameters']['amplifier_sample_rate']
t = result['t_amplifier']

# Quick filtering
emg_filtered = intan.processing.notch_filter(emg_data, fs, f0=60)
emg_filtered = intan.processing.filter_emg(emg_filtered, filter_type='bandpass',
                                            lowcut=10, highcut=500, fs=fs)

# Visualize
intan.plotting.waterfall(data=emg_filtered, channel_indices=range(64), time_vector=t,
                         plot_title='Filtered EMG Data')

Real-time Streaming from RHX Device

from intan.interface import IntanRHXDevice

# Connect to device
device = IntanRHXDevice()
device.enable_wide_channel(range(64))
device.start_streaming()

# Stream data
timestamps, data = device.stream(duration_sec=1.0)
print(f"Acquired data shape: {data.shape}")

device.close()

Train a Gesture Classifier

from intan.ml import ModelManager
import numpy as np

# Load training data
data = np.load('training_data.npz')
X_train, y_train = data['features'], data['labels']

# Train model
manager = ModelManager()
model, pca, scaler = manager.train_model(X_train, y_train,
                                          model_type='CNN', epochs=50)

# Save for later use
manager.save_model('gesture_model.keras')

Real-time Gesture Recognition

from intan.interface import IntanRHXDevice
from intan.ml import EMGRealTimePredictor
import tensorflow as tf

# Load trained model
model = tf.keras.models.load_model('gesture_model.keras')

# Initialize device
device = IntanRHXDevice(num_channels=128)
device.start_streaming()

# Create predictor
predictor = EMGRealTimePredictor(device, model, pca, mean, std, label_names)
predictor.run_prediction_loop()

# Get predictions
while True:
    prediction = predictor.get_prediction()
    if prediction:
        print(f"Gesture: {prediction['label']} ({prediction['confidence']:.1%})")

Note: the real-time predictor and some ML utilities require additional ML dependencies (TensorFlow or PyTorch) and optional C/CUDA system libraries. If you don't have those installed, the import may fail. Wrap ML usage in a try/except or install the ML extras for this project before running the example.

Example guarded pattern:

try:
  from intan.ml import EMGRealTimePredictor
except Exception as e:
  raise RuntimeError("EMGRealTimePredictor requires ML dependencies; install extras or skip this example") from e

# then proceed to create and run the predictor

Lab Streaming Layer (LSL) Integration

from intan.interface import IntanRHXDevice, LSLPublisher

# Start device
device = IntanRHXDevice(num_channels=64)
device.start_streaming()

# Publish to LSL
publisher = LSLPublisher(name='IntanEMG', stream_type='EMG',
                         channel_count=64, sample_rate=4000)

while True:
    _, data = device.stream(n_frames=40)
    publisher.push_chunk(data.T)

🗂️ Package Structure

intan/
├── io/                     # File loading (.rhd, .dat, .csv, .npz)
├── interface/              # RHX device, LSL, hardware interfaces
├── processing/             # Signal processing and filtering
├── ml/                     # Machine learning pipeline
├── plotting/               # Visualization utilities
├── applications/           # GUI applications
├── decomposition/          # PCA, ICA decomposition
└── samples/                # Sample data utilities

examples/
├── Read_Files/             # File loading examples
├── RHXDevice/              # Device streaming examples
├── LSL/                    # Lab Streaming Layer examples
├── gesture_classifier/     # ML training and prediction
├── applications/           # GUI application demos
├── 3D_printed_arm_control/ # Robotic control integration
└── interface/              # Hardware interfacing examples

🎯 Use Cases

📊 Data Analysis

Load and analyze .rhd recordings
Batch process multiple files
Extract specific time segments
Generate publication-quality figures

🔴 Real-time Applications

Live EMG visualization
Online gesture recognition
Closed-loop control systems
Synchronized multi-modal recording

🤖 Machine Learning

Train gesture classifiers
Real-time prediction
Cross-session validation
Transfer learning

🔬 Research

Impedance testing
Signal quality monitoring
Protocol automation
Custom experimental setups

📖 Documentation

Comprehensive documentation is available at neuro-mechatronics-interfaces.github.io/python-intan

Key Sections:

🎓 Examples

The examples/ directory contains 60+ working examples organized by category:

# File loading
python examples/Read_Files/load_rhd_demo.py

# Real-time streaming
python examples/RHXDevice/scrolling_live.py

# Gesture classification pipeline
python examples/gesture_classifier/1a_build_training_dataset_rhd.py
python examples/gesture_classifier/2_train_model.py
python examples/gesture_classifier/3d_predict_from_device_realtime.py

# GUI applications
python examples/applications/run_emg_viewer.py
python examples/applications/gesture_pipeline_gui.py

# LSL streaming
python examples/LSL/lsl_waveform_viewer.py
python examples/LSL/lsl_rms_barplot.py

See the Examples Documentation for complete guides.

🛠️ Supported Hardware

Intan RHX Controllers: RHD USB Interface Board, RHD Recording Controller
Amplifiers: RHD2000 series (RHD2132, RHD2164, RHD2216, etc.)
Stimulation: RHS2000 series amplifiers
Peripherals: Raspberry Pi Pico, servo controllers, IMU sensors
Integration: Lab Streaming Layer (LSL) compatible devices

🤝 Contributing

We welcome contributions! Whether it's:

🐛 Bug reports
✨ Feature requests
📝 Documentation improvements
🧪 New examples
🔧 Code contributions

Please see our Contributing Guide for details.

Ways to contribute:

Report bugs or request features via GitHub Issues
Submit pull requests with improvements
Share your use cases and examples
Help answer questions in discussions
Improve documentation

📝 Citation

If you use this package in your research, please cite:

@software{Shulgach_Python_Intan_2025,
  author = {Shulgach, Jonathan and Murphy, Max and Foy, Adrian},
  title = {{Python Intan Package}},
  year = {2025},
  month = {01},
  version = {0.0.3},
  url = {https://github.com/Neuro-Mechatronics-Interfaces/python-intan},
  note = {Neuromechatronics Lab, Carnegie Mellon University}
}

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License means:

✅ Commercial use
✅ Modification
✅ Distribution
✅ Private use

🙏 Acknowledgments

Developed by the Neuromechatronics Lab at Carnegie Mellon University.

Core Contributors:

Jonathan Shulgach
Max Murphy
Adrian Foy

Special Thanks:

Intan Technologies for hardware and support
The open-source neuroscience community

📧 Contact & Support

Documentation: neuro-mechatronics-interfaces.github.io/python-intan
Issues: GitHub Issues
Email: jshulgac@andrew.cmu.edu
Lab Website: Neuromechatronics Lab

🚦 Status & Roadmap

Current Version: 0.0.3 (January 2025)

Completed ✅

File loading (.rhd, .dat, .csv, .npz)
Real-time TCP streaming from RHX
Signal processing pipeline
Machine learning (CNN, LSTM, Dense models)
Real-time gesture recognition
Lab Streaming Layer integration
GUI applications (EMG viewer, trial selector, gesture pipeline)
Hardware integration (Pico, robotic arms, IMU)
Comprehensive documentation and examples

In Progress 🚧

Performance benchmarking suite
Extended LSL marker synchronization
Additional ML model architectures
Mobile device integration

Planned 📋

Public training datasets
Cloud integration for distributed processing
Advanced impedance testing tools
Multi-language support (MATLAB, Julia wrappers)

See CHANGELOG.md for detailed version history.

⭐ If you find this package useful, please consider giving it a star on GitHub! ⭐

Made with ❤️ by the Neuromechatronics Lab

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Python Intan

✨ Key Features

📚 Quick Links

📦 Installation

From PyPI (Recommended)

From Source (Latest Features)

With Virtual Environment

Optional Features

🚀 Getting Started

Load and Visualize EMG Data

Real-time Streaming from RHX Device

Train a Gesture Classifier

Real-time Gesture Recognition

Lab Streaming Layer (LSL) Integration

🗂️ Package Structure

🎯 Use Cases

📊 Data Analysis

🔴 Real-time Applications

🤖 Machine Learning

🔬 Research

📖 Documentation

🎓 Examples

🛠️ Supported Hardware

🤝 Contributing

📝 Citation

📄 License

🙏 Acknowledgments

📧 Contact & Support

🚦 Status & Roadmap

Completed ✅

In Progress 🚧

Planned 📋

FilesExpand file tree

README.md

Latest commit

History

README.md

File metadata and controls

Python Intan

✨ Key Features

📚 Quick Links

📦 Installation

From PyPI (Recommended)

From Source (Latest Features)

With Virtual Environment

Optional Features

🚀 Getting Started

Load and Visualize EMG Data

Real-time Streaming from RHX Device

Train a Gesture Classifier

Real-time Gesture Recognition

Lab Streaming Layer (LSL) Integration

🗂️ Package Structure

🎯 Use Cases

📊 Data Analysis

🔴 Real-time Applications

🤖 Machine Learning

🔬 Research

📖 Documentation

🎓 Examples

🛠️ Supported Hardware

🤝 Contributing

📝 Citation

📄 License

🙏 Acknowledgments

📧 Contact & Support

🚦 Status & Roadmap

Completed ✅

In Progress 🚧

Planned 📋