ANLffr

From the Systems Neuroscience of Auditory Perception lab at Purdue University, a set of tools to analyze and interpret auditory steady-state responses, particularly the subcortical kind commonly known as frequency-following responses (FFRs). In particular, the package provides code for multitaper-analysis of spectra and phase locking along with complex-principal component analysis of phase-locking for multichannel FFRs. Support for "bootstrapping" any of the included functions is also available via the boostrap module. Finally, time-frequency analysis can also be performed flexibly using the tfr module. Although originally created to analyze steady-state electrophysiological responses, the generality of the spectral analysis and time-frequency analysis function allow them to be applied widely. Typical usage would begin with:

#!/usr/bin/env python

from anlffr import spectral

# If using a Biosemi EEG system
from anlffr.helper import biosemi2mne

Get the latest code

To get the latest code using git, simply type:

git clone https://github.com/SNAPsoftware/ANLffr.git

To use a particular realease/pre-release version, say vX.X.Xyz, type:

git checkout tags/vX.X.Xy

If you don't have git installed, you can download a zip or tarball of the latest code: https://github.com/SNAPsoftware/ANLffr/archive/master.zip If you have pip, you may be able to download and install anlffr in one step using (and hence skip the "Install anlffr" steps below):

pip install git+https://github.com/SNAPsoftware/ANLffr.git --user

git remote set-url origin https://github.com/SNAPsoftware/ANLffr.git

Install anlffr

As with any Python packages, to install ANLffr, go into the ANLffr source code directory and do:

python setup.py install

or if you don't have admin access to your python setup (permission denied when install) use:

python setup.py install --user

Dependencies

The minimum required dependencies to run ANLffr are:

• Python >= 3.6
• NumPy >= 1.15.4
• SciPy >= 1.1.0

For full functionality, some functions (e.g., modules in the anlffr.helper package) require:

• Matplotlib >= 3.0.3
• MNE-Python >= 0.21

Getting Started

The examples directory contains a sample script that you could modify for your purposes. That would be a good place to get started! See Bharadwaj & Shinn-Cunningham (2014) for details of the multichannel complex-PCA method. However, note that critical issues in Bharadwaj & Shinn-Cunningham (2014) were found and described in Lu et al., 2020. For details of the PLV computation and bootstrapping, see Zhu et al., (2013).

Briefly, consider the following example:

#!/usr/bin/env python

# If you already have a data.mat file with data

# Using scipy to load the data
from scipy import io
dat = io.loadmat(path_to_data + 'data.mat')

# Say the variable containing the data array is 'x'
x = dat['x']

# View to check if 'x' is in the right format
x.shape

# Import the spectral module to calculate PLV
from anlffr import spectral

# Set parameters for the PLV computation
# Sampling rate is 4096 Hz
# Frequencies of interest are with 5 and 500 Hz
# Calculate inter-trial coherence instead of PLV
# Zero-pad data to the next power of 2 for fast FFT
# Use 3 tapers with the time-half bandwidth product of 2
params = dict(Fs = 4096, tapers = [2, 3], fpass = [5, 500], itc = 1,
              pad = 1)

# Actually compute the phase-locking measure (ITC here)
(plv, f) = spectral.mtplv(x, params)

# Plot results for channel number 31 (index 30)
# Use pylab for plotting

import pylab as pl
pl.plot(f, plv[30,:],linewidth = 2)
pl.xlabel('Frequency (Hz)', fontsize = 20)
pl.ylabel('Inter-Trial Coherence')
pl.show()

Licensing

ANLffr is BSD-licenced (3 clause):

This software is OSI Certified Open Source Software. OSI Certified is a certification mark of the Open Source Initiative.

Copyright (c) 2013-2021, authors of ANLffr. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

• Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
• Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
• Neither the names of ANLffr authors nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission.

This software is provided by the copyright holders and contributors "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the copyright owner or contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage.