Function to calculate consistency of phase at a given frequency across measurements
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
CODE_OF_CONDUCT.md
CONTRIBUTING.md
LICENSE
PhaseCoherence.py
README.md

README.md

Phase-Coherence-for-Python

Function to calculate consistency of phase at a given frequency across measurements

About the method

Some background

This function calculates phase coherence at a frequency of interest, across different measurements. The phase coherence value indexes how consistent the phase is across different measurements. The phase coherence value lies between 0 and 1: 1 indicates perfect phase coherence and 0 indicates no phase coherence.

This method is often used in electro-encephalography (EEG) analyses to determine the phase consistency of the neural response at a particular frequency, across trials. For example, researchers often choose to investigate phase consistency at the frequency of a presented stimulus. Note that the same method is sometimes referred to as the phase locking value (PLV)--although some implementations of the PLV use a time-series filter rather than conducting the analyses in the frequency domain, as used here.

The method is described in the following paper:

Picton, T. W., John, M. S., Dimitrijevic, A., & Purcell, D. (2003). Human auditory steady-state responses: Respuestas auditivas de estado estable en humanos. International journal of audiology, 42(4), 177-219.

Extentions of the method

This method could be applied to examine patterns in time-series data other than neural data.

Getting started

Prerequisites

The code was written using using Python 3.6.1. The code requires numpy.

Running the Python script

The Python code is contained within the following file: PhaseCoherence.py

The script takes three inputs:

  1. freq - this is a floating point value indicating the frequency to be analysed, in Hertz, e.g. 50.
  2. timeSeries - this is the data to be analysed; should be a 2-dimensional matrix of size: (number of trials/measurements) x (number of time points)
  3. FS - this is the sampling rate of the time-series data, in Hertz, e.g. 1000.

The code can be run as follows:

PC = PhaseCoherence(freq, timeSeries, FS)

The output from the script is a floating-point value between 0 and 1.

Note for MATLAB users

There are two versions of the code, which perform the same computations and output: one for MATLAB and one for Python 3. The MATLAB code can be found in a separate repository: Phase-Coherence-for-MATLAB

License

This project is licensed under the MIT License; see the LICENSE file for details.

This project can be cited using the following DOI: DOI