ERPparam

ERPparam is tool to parameterize neural evoked responses.

Quickstart

Fitting a Single Evoked Response

With an event-related potential (ERP) loaded (with 'time' storing time points, and 'signal' storing the ERP, ERPparam can be used as follows:

# Import the ERPparam object
from ERPparam import ERPparam

# Initialize ERPparam object
params = ERPparam()

# Define time range within which to model the ERP
time_range = [0, 1]

# Model the ERP with ERPparam, and print out a report
params.report(time, signal, time_range)

ERPparam.report() fits the model, plots the original (or rectified) ERP with the associated ERPparam model fit, and prints out the parameters of the model fit.

Example output for the report of a ERPparam fit on an individual ERP:

Defining the model Settings

The settings for the algorithm are:

• peak_width_limits sets the possible lower- and upper-bounds for the fitted peak widths.
• max_n_peaks sets the maximum number of peaks to fit.
• min_peak_height sets an absolute limit on the minimum height (above aperiodic) for any extracted peak.
• peak_threshold sets a relative threshold above which a peak height must cross to be included in the model.

These settings can be defined when initializing the model, for example:

# Initialize an ERPparam model object with defined settings
params = ERPparam(peak_width_limits=(0.01, 0.5), peak_threshold=2, min_peak_height=0, max_n_peaks=3,)

Fitting a Group of ERPs

Next is an example workflow for fitting a group of ERPs. In this case, 'time' is again a 1D array of time points, and 'signal' is a 2D array of ERPs. We can fit the group of ERPs by doing:

# Initialize a ERPparamGroup object, specifying some parameters
params = ERPparamGroup(peak_width_limits=(0.01, 0.5), peak_threshold=2, min_peak_height=0, max_n_peaks=3)

# Fit ERPparam model across the matrix of ERPs
params.fit(time, signals)

# Create and save out a report summarizing the results across the group of ERPs
params.save_report('example_report')

# Save out ERPparamGroup results for further analysis later
params.save(file_name='group_results', save_results=True)

Example output from using ERPparamGroup across a group of ERPs: