Metadata-Version: 1.1
Name: nmb_eeg
Version: 0.9
Summary: Power spectra of pure EEG from two temporarily paralysed subjects from Whitham et al 2007
Home-page: https://github.com/berndporr/nmb_eeg
Author: Bernd Porr
Author-email: bernd.porr@glasgow.ac.uk
License: GPL 3.0
Description: 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 a , Kenneth J. Pope b , Sean P. Fitzgibbon c , Trent Lewis b ,
        C. Richard Clark c , Stephen Loveless d , Marita Broberg e , Angus Wallace e ,
        Dylan DeLosAngeles e , Peter Lillie f , Andrew Hardy f , Rik.
        Clinical Neurophysiology Volume 118, Issue 8, August 2007,
        Pages 1877-1888. <https://www.sciencedirect.com/science/article/abs/pii/S1388245707001988>`_
        
        Please cite as "Data from ..." as outlined above. This has been advised by Elsevier's Copyrights Coordinator.
        
        
        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.
        
        
        
        Credit
        ------
        
        Bernd Porr <bernd.porr@glasgow.ac.uk>
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
