Getting Started with EMGFlow

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)

Input data format

EMGFlow accepts data in plaintext .CSV file format. Files should have the following format:

• Row 1 - Column headers
• Col 1 - Labelled Time, and contains the timestamps of sampled data
• Col 2:n - Assumed to be sEMG or related signal data.

As an example, here is the first few rows of some sample data included in the package:

Time	EMG_zyg	EMG_cor
50.0005	-0.145569	-0.097046
50.0010	-0.129089	-0.100708
50.0015	-0.118713	-0.101929
50.0020	-0.118103	-0.104675
50.0025	-0.104370	-0.097656
...	...	...

The "Time" column can generally be omitted when the sampling rate is known, though some functions require a "Time" column.

EMGFlow Pipeline Processing Steps

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