LibEMG

The goal of this library is to provide an easy to use and feature-rich API for developing robust real-time EMG-based interactions, and performing thorough offline analysis.

Authors: Ethan Eddy, Evan Campbell, Angkoon Phinyomark, Scott Bateman, and Erik Scheme

Installing the Latest Release

$ pip install libemg

Note: Not yet published as part of this submission.

Citing

We ask that if you leverage this library for any research related purposes please cite the following publication:

Citation

This API documentation is split into the following six sections:

Introduction:

• Introduction

Modules:

• Data
• Filtering
• Feature Extraction
• Feature Selection
• Classification
• Evaluation

Tools:

• Screen Guided Training
• Supported Hardware
• Visualizations

API:

• Data Handling
• Filtering
• Feature Extraction
• Feature Selection
• Classification
• Offline Evaluation Metrics
• Utils
• Streamers
• Screen Guided Training

Offline Examples:

• Simple Offline Analysis
• Exploring Features
• Optimizing Feature Params
• Deep Learning

Online Examples:

• Continuous Control (Snake)
• Cross Platform Control (Unity)
• Proportional Control (Mouse Interface)
• Mixed Reality (Hololens 2)
• Online Evaluation (Iso Fitts)