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)

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 with added NaN values:

Time	EMG_zyg	EMG_cor
0.0005	-0.145569	-0.097046
0.0010	-0.129089	-0.100708
0.0015	-0.118713	-0.101929
0.0020	NaN	NaN
0.0025	-0.104370	-0.097656
...	...	...

For proper calculations, ensure the 'Time' column has an equal difference between each sequential row.

EMGFlow uses pd.read_csv() from the Pandas package to read in files. Continuing with the documentation for Pandas, the following values are interpreted as NaN:

• " "
• "#N/A"
• "#N/A N/A"
• "#NA"
• "-1.#IND"
• "-1.#QNAN"
• "-NaN"
• "-nan"
• "1.#IND"
• "1.#QNAN"
• "<NA>"
• "N/A"
• "NA"
• "NULL"
• "NaN"
• "None"
• "n/a"
• "nan"
• "null"

In the future, we plan to make EMGFlow compatible with other file formats.

EMGFlow Pipeline Processing Steps

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

Sample Data Files

The sample data used in EMGFlow is taken from PeakAffectDS.

PeakAffectDS Name	EMGFlow Name	Timeframe
01-03-01.csv	sample_data_01.csv	50.0005s : 60.0000s
01-04-01.csv	sample_data_02.csv	130.0005s : 140.0000s
02-06-02.csv	sample_data_03.csv	10.0005s : 20.0000s
02-07-02.csv	sample_data_04.csv	15.0005s : 25.0000s