Pure EEG power during paralysis

Power spectra of pure EEG from two temporarily paralysed subjects.

Data from (Fig 1, B-traces):

Scalp electrical recording during paralysis: Quantitative evidence that EEG frequencies above 20 Hz are contaminated by EMG Emma M. Whitham, Kenneth J. Pope, Sean P. Fitzgibbon, Trent Lewis, C. Richard Clark, Stephen Loveless, Marita Broberg, Angus Wallace, Dylan DeLosAngeles, Peter Lillie, Andrew Hardy, Rik. Clinical Neurophysiology Volume 118, Issue 8, August 2007, Pages 1877-1888.

I've contacted Elsevier's Copyrights Coordinator and he confirmed that there is no need to obtain formal permission from Elsevier. You merely need to acknowledge the original source. Please cite as "Data from Whitham 2007 ..." (see https://www.elsevier.com/about/policies/copyright/permissions).

Installation

Locally by just running python setup.py install or via pypi:

pip install nmb_eeg

Usage

To obtain the average PSD over all experiments just use the default constructor:

p = NMB_EEG_From_WhithamEtAl()

If you want to extract the PSD of dataset one do:

p = NMB_EEG_From_WhithamEtAl(1)

Obtain the power spectral density in V^2/Hz use:

psd = p.EEGVariance(f)

where f can be either a single frequency or a numpy array. The lowest permitted frequency is f_signal_min and the highest f_signal_max.

The total power of the entire frequency range from f_signal_min to f_signal_max is:

totalEEGPower = p.totalEEGPower()

Because EEGVariance(f) accepts a numpy array plotting the spectrum is simply:

f = np.linspace(p.f_signal_min,p.f_signal_max,100)
plt.plot(f,p.EEGVariance(f))

Usage example

Run:

plot_paralysed_EEG_PSD.py

which generates the plot at the top of this page.

Class reference

    class NMB_EEG_From_WhithamEtAl(builtins.object)
     |  NMB_EEG_From_WhithamEtAl(datasetIndex=-1, degree=15)
     |  
     |  __init__(self, datasetIndex=-1, degree=15)
     |      Constructor which prepares the power spectrum from Figure `datasetIndex` from Whitham et al 2007
     |      using a polynomial fit. The default degreee is 15. If no datasetIndex is
     |      given or is negative then the average from all 6 figures from the paper is calculated.
     |  
     |  EEGVariance(self, frequency)
     |      Returns the EEG variance (i.e. power spectral density) in V^2/Hz at the specified frequency or 
     |      frequency ranges if frequency is a numpy array. The lowest permitted frequency is
     |      `f_signal_min` and the highest `f_signal_max`.
     |  
     |  totalEEGPower(self)
     |      Calculates the total power of the EEG in V^2 between `f_signal_min` and `f_signal_max`.
     |  
     |  allsubjectdesciption = ['Subject 1, 1st trace', 'Subject 1, 2nd trace'...
     |  
     |  f_signal_max = 95
     |  
     |  f_signal_min = 1

Credit

Bernd Porr bernd.porr@glasgow.ac.uk