Processing Steps

Download

EMGFlow can be installed from PyPI:

pip install EMGFlow

Quick example

EMGFlow extracts a comprehensive set of 32 statistical features from sEMG signals, achieved with only a few lines of code:

import EMGFlow as ef

# Get path dictionary
path_names = ef.make_paths()

# Load sample data
ef.make_sample_data(path_names)

# Preprocess signals
ef.clean_signals(path_names, sampling_rate=2000)

# Plot data on the "EMG_zyg" column
ef.plot_dashboard(path_names, 'EMG_zyg', 'mV')

# Extract features and save results in "Features.csv" in feature_path
df = ef.extract_features(path_names, sampling_rate=2000)

EMGFlow Pipeline Processing Steps

Figure 1. Decomposition of the EMG signal into individual frequency components.