EMG#

Main#

emg_process()#

emg_process(emg_signal, sampling_rate=1000, report=None, **kwargs)[source]#

Process a electromyography (EMG) signal

Convenience function that automatically processes an electromyography signal.

Parameters:
  • emg_signal (Union[list, np.array, pd.Series]) – The raw electromyography channel.

  • sampling_rate (int) – The sampling frequency of emg_signal (in Hz, i.e., samples/second).

  • report (str) – The filename of a report containing description and figures of processing (e.g. "myreport.html"). Needs to be supplied if a report file should be generated. Defaults to None. Can also be "text" to just print the text in the console without saving anything.

  • **kwargs – Other arguments to be passed to specific methods. For more information, see emg_methods().

Returns:

  • signals (DataFrame) – A DataFrame of same length as emg_signal containing the following columns:

    • "EMG_Raw": the raw signal.

    • "EMG_Clean": the cleaned signal.

    • "EMG_Amplitude": the signal amplitude, or the activation level of the signal.

    • "EMG_Activity": the activity of the signal for which amplitude exceeds the threshold specified,marked as “1” in a list of zeros.

    • "EMG_Onsets": the onsets of the amplitude, marked as “1” in a list of zeros.

    • "EMG_Offsets": the offsets of the amplitude, marked as “1” in a list of zeros.

  • info (dict) – A dictionary containing the information of each amplitude onset, offset, and peak activity (see emg_activation()), as well as the signals’ sampling rate.

Examples

In [1]: import neurokit2 as nk

In [2]: emg = nk.emg_simulate(duration=10, sampling_rate=1000, burst_number=3)

In [3]: signals, info = nk.emg_process(emg, sampling_rate=1000)

In [4]: nk.emg_plot(signals, info)
savefig/p_emg_process1.png

emg_analyze()#

emg_analyze(data, sampling_rate=1000, method='auto')[source]#

EMG Analysis

Performs EMG analysis on either epochs (event-related analysis) or on longer periods of data such as resting-state data.

Parameters:
  • data (Union[dict, pd.DataFrame]) – A dictionary of epochs, containing one DataFrame per epoch, usually obtained via epochs_create(), or a DataFrame containing all epochs, usually obtained via epochs_to_df(). Can also take a DataFrame of processed signals from a longer period of data, typically generated by emg_process() or bio_process(). Can also take a dict containing sets of separate periods of data.

  • sampling_rate (int) – The sampling frequency of the signal (in Hz, i.e., samples/second). Defaults to 1000Hz.

  • method (str) – Can be one of "event-related" for event-related analysis on epochs, or "interval-related" for analysis on longer periods of data. Defaults to auto where the right method will be chosen based on the mean duration of the data ("event-related" for duration under 10s).

Returns:

DataFrame – A dataframe containing the analyzed EMG features. If event-related analysis is conducted, each epoch is indicated by the Label column. See emg_eventrelated() and emg_intervalrelated() docstrings for details.

Examples

In [1]: import neurokit2 as nk

In [2]: import pandas as pd

# Example with simulated data
In [3]: emg = nk.emg_simulate(duration=20, sampling_rate=1000, burst_number=3)

In [4]: emg_signals, info = nk.emg_process(emg, sampling_rate=1000)

In [5]: epochs = nk.epochs_create(emg_signals, events=[3000, 6000, 9000], sampling_rate=1000,
   ...:                           epochs_start=-0.1, epochs_end=1.9)
   ...: 

# Event-related analysis
In [6]: analyze_epochs = nk.emg_analyze(epochs, method="event-related")

In [7]: analyze_epochs
Out[7]: 
  Label  Event_Onset  ...  EMG_Amplitude_Max_Time  EMG_Bursts
1     1         3000  ...                1.693897         1.0
2     2         6000  ...                     NaN         NaN
3     3         9000  ...                0.753427         1.0

[3 rows x 8 columns]

# Interval-related analysis
In [8]: analyze_df = nk.emg_analyze(emg_signals, method="interval-related")

In [9]: analyze_df
Out[9]: 
   EMG_Activation_N  EMG_Amplitude_Mean
0               3.0            0.233858

emg_simulate()#

emg_simulate(duration=10, length=None, sampling_rate=1000, noise=0.01, burst_number=1, burst_duration=1.0, random_state=None)[source]#

Simulate an EMG signal

Generate an artificial (synthetic) EMG signal of a given duration and sampling rate.

Parameters:
  • duration (int) – Desired recording length in seconds.

  • sampling_rate (int) – The desired sampling rate (in Hz, i.e., samples/second).

  • length (int) – The desired length of the signal (in samples).

  • noise (float) – Noise level (gaussian noise).

  • burst_number (int) – Desired number of bursts of activity (active muscle periods).

  • burst_duration (float or list) – Duration of the bursts. Can be a float (each burst will have the same duration) or a list of durations for each bursts.

  • random_state (None, int, numpy.random.RandomState or numpy.random.Generator) – Seed for the random number generator. See for misc.check_random_state for further information.

Returns:

array – Vector containing the EMG signal.

Examples

In [1]: import neurokit2 as nk

In [2]: import pandas as pd

In [3]: emg = nk.emg_simulate(duration=10, burst_number=3)

In [4]: fig = nk.signal_plot(emg)
savefig/p_emg_simulate1.png

See also

ecg_simulate, rsp_simulate, eda_simulate, ppg_simulate

References

This function is based on this script.

emg_plot()#

emg_plot(emg_signals, info=None, static=True)[source]#

EMG Graph

Visualize electromyography (EMG) data.

Parameters:
  • emg_signals (DataFrame) – DataFrame obtained from emg_process().

  • info (dict) – The information Dict returned by emg_process(). Defaults to None.

  • static (bool) – If True, a static plot will be generated with matplotlib. If False, an interactive plot will be generated with plotly. Defaults to True.

See also

emg_process

Returns:

See ecg_plot() for details on how to access the figure, modify the size and save it.

Examples

In [1]: import neurokit2 as nk

# Simulate data
In [2]: emg = nk.emg_simulate(duration=10, sampling_rate=1000, burst_number=3)

# Process signal
In [3]: emg_signals, info = nk.emg_process(emg, sampling_rate=1000)

# Plot
In [4]: nk.emg_plot(emg_signals, info)
savefig/p_emg_plot.png

Preprocessing#

emg_clean()#

emg_clean(emg_signal, sampling_rate=1000, method='biosppy')[source]#

Preprocess an electromyography (emg) signal

Clean an EMG signal using a set of parameters. Only one method is available at the moment.

  • BioSPPy: fourth order 100 Hz highpass Butterworth filter followed by a constant detrending.

Parameters:
  • emg_signal (Union[list, np.array, pd.Series]) – The raw EMG channel.

  • sampling_rate (int) – The sampling frequency of emg_signal (in Hz, i.e., samples/second). Defaults to 1000.

  • method (str) – The processing pipeline to apply. Can be one of "biosppy" or "none". Defaults to "biosppy". If "none" is passed, the raw signal will be returned without any cleaning.

Returns:

array – Vector containing the cleaned EMG signal.

Examples

In [1]: import pandas as pd

In [2]: import neurokit2 as nk

In [3]: emg = nk.emg_simulate(duration=10, sampling_rate=1000)

In [4]: signals = pd.DataFrame({"EMG_Raw": emg, "EMG_Cleaned":nk.emg_clean(emg, sampling_rate=1000)})

In [5]: fig = signals.plot()
savefig/p_emg_clean1.png

emg_amplitude()#

emg_amplitude(emg_cleaned)[source]#

Compute electromyography (EMG) amplitude

Compute electromyography amplitude given the cleaned respiration signal, done by calculating the linear envelope of the signal.

Parameters:

emg_cleaned (Union[list, np.array, pd.Series]) – The cleaned electromyography channel as returned by emg_clean().

Returns:

array – A vector containing the electromyography amplitude.

See also

emg_clean, emg_rate, emg_process, emg_plot

Examples

In [1]: import neurokit2 as nk

In [2]: import pandas as pd

In [3]: emg = nk.emg_simulate(duration=10, sampling_rate=1000, burst_number=3)

In [4]: cleaned = nk.emg_clean(emg, sampling_rate=1000)

In [5]: amplitude = nk.emg_amplitude(cleaned)

In [6]: fig = pd.DataFrame({"EMG": emg, "Amplitude": amplitude}).plot(subplots=True)
savefig/p_emg_amplitude1.png

emg_activation()#

emg_activation(emg_amplitude=None, emg_cleaned=None, sampling_rate=1000, method='threshold', threshold='default', duration_min='default', size=None, threshold_size=None, **kwargs)[source]#

Locate EMG Activity

Detects onset in EMG signal based on the amplitude threshold.

Parameters:
  • emg_amplitude (array) – At least one EMG-related signal. Either the amplitude of the EMG signal, obtained from emg_amplitude() for methods like "threshold" or "mixture"), and / or the cleaned EMG signal (for methods like "pelt", "biosppy" or "silva").

  • emg_cleaned (array) – At least one EMG-related signal. Either the amplitude of the EMG signal, obtained from emg_amplitude() for methods like "threshold" or "mixture"), and / or the cleaned EMG signal (for methods like "pelt", "biosppy" or "silva").

  • sampling_rate (int) – The sampling frequency of emg_signal (in Hz, i.e., samples/second).

  • method (str) – The algorithm used to discriminate between activity and baseline. Can be one of "mixture" (default) or "threshold". If "mixture", will use a Gaussian Mixture Model to categorize between the two states. If "threshold", will consider as activated all points which amplitude is superior to the threshold. Can also be "pelt" or "biosppy" or "silva".

  • threshold (str) – If method is "mixture", then it corresponds to the minimum probability required to be considered as activated (default to 0.33). If method is "threshold", then it corresponds to the minimum amplitude to detect as onset i.e., defaults to one tenth of the standard deviation of emg_amplitude. If method is "silva", defaults to 0.05. If method is "biosppy", defaults to 1.2 times of the mean of the absolute of the smoothed, full-wave-rectified signal. If method is "pelt", threshold defaults to None as changepoints are used as a basis for detection.

  • duration_min (float) – The minimum duration of a period of activity or non-activity in seconds. If default, will be set to 0.05 (50 ms).

  • size (float or int) – Detection window size (seconds). Applicable only if method is "biosppy" or "silva". If None, defaults to 0.05 for "biosppy" and 20 for "silva".

  • threshold_size (int) – Window size for calculation of the adaptive threshold. Must be bigger than the detection window size. Applicable only if method is "silva”. If None, defaults to 22.

  • kwargs (optional) – Other arguments.

Returns:

  • info (dict) – A dictionary containing additional information, in this case the samples at which the onsets, offsets, and periods of activations of the EMG signal occur, accessible with the key "EMG_Onsets", "EMG_Offsets", and "EMG_Activity" respectively.

  • activity_signal (DataFrame) – A DataFrame of same length as the input signal in which occurences of onsets, offsets, and activity (above the threshold) of the EMG signal are marked as “1” in lists of zeros with the same length as emg_amplitude. Accessible with the keys "EMG_Onsets", "EMG_Offsets", and "EMG_Activity" respectively.

Examples

In [1]: import neurokit2 as nk

# Simulate signal and obtain amplitude
In [2]: emg = nk.emg_simulate(duration=10, burst_number=3)

In [3]: emg_cleaned = nk.emg_clean(emg)

In [4]: emg_amplitude = nk.emg_amplitude(emg_cleaned)
  • Example 1: Threshold method

In [5]: activity, info = nk.emg_activation(emg_amplitude=emg_amplitude, method="threshold")

In [6]: nk.events_plot([info["EMG_Offsets"], info["EMG_Onsets"]], emg_cleaned)
savefig/p_emg_activation1.png
  • Example 2: Pelt method

In [7]: activity, info = nk.emg_activation(emg_cleaned=emg_cleaned, method="pelt")

In [8]: nk.events_plot([info["EMG_Offsets"], info["EMG_Onsets"]], emg_cleaned)
savefig/p_emg_activation2.png
  • Example 3: Biosppy method

In [9]: activity, info = nk.emg_activation(emg_cleaned=emg_cleaned, method="biosppy")

In [10]: nk.events_plot([info["EMG_Offsets"], info["EMG_Onsets"]], emg_cleaned)
savefig/p_emg_activation3.png
  • Example 4: Silva method

In [11]: activity, info = nk.emg_activation(emg_cleaned=emg_cleaned, method="silva")

In [12]: nk.events_plot([info["EMG_Offsets"], info["EMG_Onsets"]], emg_cleaned)
savefig/p_emg_activation4.png

References

  • Silva H, Scherer R, Sousa J, Londral A , “Towards improving the ssability of electromyographic interfacess”, Journal of Oral Rehabilitation, pp. 1-2, 2012.

Analysis#

emg_eventrelated()#

emg_eventrelated(epochs, silent=False)[source]#

Event-related EMG Analysis

Performs event-related EMG analysis on epochs.

Parameters:
  • epochs (Union[dict, pd.DataFrame]) – A dict containing one DataFrame per event/trial, usually obtained via epochs_create(), or a DataFrame containing all epochs, usually obtained via epochs_to_df().

  • silent (bool) – If True, silence possible warnings.

Returns:

DataFrame – A dataframe containing the analyzed EMG features for each epoch, with each epoch indicated by the Label column (if not present, by the Index column). The analyzed features consist of the following:

  • "EMG_Activation*: indication of whether there is muscular activation following the onset of the event (1 if present, 0 if absent) and if so, its corresponding amplitude features and the number of activations in each epoch. If there is no activation, nans are displayed for the below features.

  • "EMG_Amplitude_Mean*: the mean amplitude of the activity.

  • "EMG_Amplitude_Max*: the maximum amplitude of the activity.

  • "EMG_Amplitude_SD*: the standard deviation of the activity amplitude.

  • "EMG_Amplitude_Max_Time*: the time of maximum amplitude.

  • "EMG_Bursts*: the number of activations, or bursts of activity, within each epoch.

Examples

In [1]: import neurokit2 as nk

# Example with simulated data
In [2]: emg = nk.emg_simulate(duration=20, sampling_rate=1000, burst_number=3)

In [3]: emg_signals, info = nk.emg_process(emg, sampling_rate=1000)

In [4]: epochs = nk.epochs_create(emg_signals, events=[3000, 6000, 9000], sampling_rate=1000,
   ...:                           epochs_start=-0.1,epochs_end=1.9)
   ...: 

In [5]: nk.emg_eventrelated(epochs)
Out[5]: 
  Label  Event_Onset  ...  EMG_Amplitude_Max_Time  EMG_Bursts
1     1         3000  ...                1.301701         1.0
2     2         6000  ...                     NaN         NaN
3     3         9000  ...                1.316708         1.0

[3 rows x 8 columns]

emg_intervalrelated()#

emg_intervalrelated(data)[source]#

EMG Analysis for Interval-related Data

Performs EMG analysis on longer periods of data (typically > 10 seconds), such as resting-state data.

Parameters:

data (Union[dict, pd.DataFrame]) – A DataFrame containing the different processed signal(s) as different columns, typically generated by emg_process() or bio_process(). Can also take a dict containing sets of separately processed DataFrames.

Returns:

DataFrame – A dataframe containing the analyzed EMG features. The analyzed features consist of the following: * "EMG_Activation_N": the number of bursts of muscular activity. * "EMG_Amplitude_Mean": the mean amplitude of the muscular activity.

Examples

In [1]: import neurokit2 as nk

# Example with simulated data
In [2]: emg = nk.emg_simulate(duration=40, sampling_rate=1000, burst_number=3)

In [3]: emg_signals, info = nk.emg_process(emg, sampling_rate=1000)

# Single dataframe is passed
In [4]: nk.emg_intervalrelated(emg_signals)
Out[4]: 
   EMG_Activation_N  EMG_Amplitude_Mean
0               3.0            0.245069

In [5]: epochs = nk.epochs_create(emg_signals, events=[0, 20000], sampling_rate=1000, epochs_end=20)

In [6]: nk.emg_intervalrelated(epochs)
Out[6]: 
  Label  EMG_Activation_N  EMG_Amplitude_Mean
1     1                 2            0.248066
2     2                 1            0.242081

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Submodule for NeuroKit.