notebook.community

Edit and run

Result of comparing the SNR with and without EEG





In [5]:



    


import mne
import numpy as np
from glob import glob
import matplotlib.pyplot as plt



    











In [6]:



    


%matplotlib inline 
plt.style.use("seaborn")



    











In [7]:



    


# paths 
snr_path = "/Volumes/projects/MINDLAB2016_MEG-Language-PD-DBS/scratch/mje/hilbert_data/hilbert_mne/snr/"
epochs_path = "/Volumes/projects/MINDLAB2016_MEG-Language-PD-DBS/scratch/mje/hilbert_data/hilbert_epochs/"



    











In [9]:



    


epochs = mne.read_epochs(epochs_path + "0010_Alpha_ds_ica-epo.fif", preload=False)
times = epochs.times * 1e3



    


















    






Reading /Volumes/projects/MINDLAB2016_MEG-Language-PD-DBS/scratch/mje/hilbert_data/hilbert_epochs/0010_Alpha_ds_ica-epo.fif ...
    Read a total of 1 projection items:
        Average EEG reference (1 x 71) active
    Found the data of interest:
        t =     -99.61 ...     900.39 ms
        0 CTF compensation matrices available
2400 matching events found
Not setting metadata
Created an SSP operator (subspace dimension = 1)
2400 matching events found
Not setting metadata
Created an SSP operator (subspace dimension = 1)
1 projection items activated

















In [10]:



    


%pylab inline
pylab.rcParams['figure.figsize'] = (15, 10)



    


















    






Populating the interactive namespace from numpy and matplotlib

















In [11]:



    


band = "Alpha"

files_meg = glob(snr_path + "*_%s_snr_meg.npy" % band)
files_meg.sort()
files_full = glob(snr_path + "*_%s_snr_full.npy" % band)
files_full.sort()

files_est_meg = glob(snr_path + "*_%s_snr_est_meg.npy" % band)
files_est_meg.sort()
files_est_full = glob(snr_path + "*_%s_snr_est_full.npy" % band)
files_est_full.sort()

snr_meg = np.asarray([np.load(f) for f in files_meg])
snr_full = np.asarray([np.load(f) for f in files_full])
snr_est_meg = np.asarray([np.load(f) for f in files_est_meg])
snr_est_full = np.asarray([np.load(f) for f in files_est_full])


plt.figure()
plt.subplot(2,1, 1)
plt.plot(times, snr_full.mean(axis=0), 'b', label="MEG + EEG", linewidth=3)
plt.plot(times, snr_meg.mean(axis=0), 'r', label="MEG only", linewidth=3)
plt.legend()
plt.subplot(2,1,2)
plt.plot(times, snr_est_full.mean(axis=0), 'b', label="EST: MEG + EEG", linewidth=3)
plt.plot(times, snr_est_meg.mean(axis=0), 'r', label="EST: MEG only", linewidth=3)
plt.legend()



    


















    
Out[11]:








<matplotlib.legend.Legend at 0x10de62290>










    
























In [12]:



    


band = "Beta"

files_meg = glob(snr_path + "*_%s_snr_meg.npy" % band)
files_meg.sort()
files_full = glob(snr_path + "*_%s_snr_full.npy" % band)
files_full.sort()

files_est_meg = glob(snr_path + "*_%s_snr_est_meg.npy" % band)
files_est_meg.sort()
files_est_full = glob(snr_path + "*_%s_snr_est_full.npy" % band)
files_est_full.sort()

snr_meg = np.asarray([np.load(f) for f in files_meg])
snr_full = np.asarray([np.load(f) for f in files_full])
snr_est_meg = np.asarray([np.load(f) for f in files_est_meg])
snr_est_full = np.asarray([np.load(f) for f in files_est_full])


plt.figure()
plt.subplot(2,1, 1)
plt.plot(times, snr_full.mean(axis=0), 'b', label="MEG + EEG", linewidth=3)
plt.plot(times, snr_meg.mean(axis=0), 'r', label="MEG only", linewidth=3)
plt.legend()
plt.subplot(2,1,2)
plt.plot(times, snr_est_full.mean(axis=0), 'b', label="EST: MEG + EEG", linewidth=3)
plt.plot(times, snr_est_meg.mean(axis=0), 'r', label="EST: MEG only", linewidth=3)
plt.legend()



    


















    
Out[12]:








<matplotlib.legend.Legend at 0x1093c3f90>










    
























In [13]:



    


band = "Gamma_low"

files_meg = glob(snr_path + "*_%s_snr_meg.npy" % band)
files_meg.sort()
files_full = glob(snr_path + "*_%s_snr_full.npy" % band)
files_full.sort()

files_est_meg = glob(snr_path + "*_%s_snr_est_meg.npy" % band)
files_est_meg.sort()
files_est_full = glob(snr_path + "*_%s_snr_est_full.npy" % band)
files_est_full.sort()

snr_meg = np.asarray([np.load(f) for f in files_meg])
snr_full = np.asarray([np.load(f) for f in files_full])
snr_est_meg = np.asarray([np.load(f) for f in files_est_meg])
snr_est_full = np.asarray([np.load(f) for f in files_est_full])


plt.figure()
plt.subplot(2,1, 1)
plt.plot(times, snr_full.mean(axis=0), 'b', label="MEG + EEG", linewidth=3)
plt.plot(times, snr_meg.mean(axis=0), 'r', label="MEG only", linewidth=3)
plt.legend()
plt.subplot(2,1,2)
plt.plot(times, snr_est_full.mean(axis=0), 'b', label="EST: MEG + EEG", linewidth=3)
plt.plot(times, snr_est_meg.mean(axis=0), 'r', label="EST: MEG only", linewidth=3)
plt.legend()



    


















    
Out[13]:








<matplotlib.legend.Legend at 0x10dd65c90>










    
























In [14]:



    


band = "Gamma_high_1"

files_meg = glob(snr_path + "*_%s_snr_meg.npy" % band)
files_meg.sort()
files_full = glob(snr_path + "*_%s_snr_full.npy" % band)
files_full.sort()

files_est_meg = glob(snr_path + "*_%s_snr_est_meg.npy" % band)
files_est_meg.sort()
files_est_full = glob(snr_path + "*_%s_snr_est_full.npy" % band)
files_est_full.sort()

snr_meg = np.asarray([np.load(f) for f in files_meg])
snr_full = np.asarray([np.load(f) for f in files_full])
snr_est_meg = np.asarray([np.load(f) for f in files_est_meg])
snr_est_full = np.asarray([np.load(f) for f in files_est_full])


plt.figure()
plt.subplot(2,1, 1)
plt.plot(times, snr_full.mean(axis=0), 'b', label="MEG + EEG", linewidth=3)
plt.plot(times, snr_meg.mean(axis=0), 'r', label="MEG only", linewidth=3)
plt.legend()
plt.subplot(2,1,2)
plt.plot(times, snr_est_full.mean(axis=0), 'b', label="EST: MEG + EEG", linewidth=3)
plt.plot(times, snr_est_meg.mean(axis=0), 'r', label="EST: MEG only", linewidth=3)
plt.legend()



    


















    
Out[14]:








<matplotlib.legend.Legend at 0x10e654050>










    
























In [15]:



    


band = "Gamma_high_2"

files_meg = glob(snr_path + "*_%s_snr_meg.npy" % band)
files_meg.sort()
files_full = glob(snr_path + "*_%s_snr_full.npy" % band)
files_full.sort()

files_est_meg = glob(snr_path + "*_%s_snr_est_meg.npy" % band)
files_est_meg.sort()
files_est_full = glob(snr_path + "*_%s_snr_est_full.npy" % band)
files_est_full.sort()

snr_meg = np.asarray([np.load(f) for f in files_meg])
snr_full = np.asarray([np.load(f) for f in files_full])
snr_est_meg = np.asarray([np.load(f) for f in files_est_meg])
snr_est_full = np.asarray([np.load(f) for f in files_est_full])


plt.figure()
plt.subplot(2,1, 1)
plt.plot(times, snr_full.mean(axis=0), 'b', label="MEG + EEG", linewidth=3)
plt.plot(times, snr_meg.mean(axis=0), 'r', label="MEG only", linewidth=3)
plt.legend()
plt.subplot(2,1,2)
plt.plot(times, snr_est_full.mean(axis=0), 'b', label="EST: MEG + EEG", linewidth=3)
plt.plot(times, snr_est_meg.mean(axis=0), 'r', label="EST: MEG only", linewidth=3)
plt.legend()



    


















    
Out[15]:








<matplotlib.legend.Legend at 0x10ecc11d0>










    
























In [ ]:

Content source: MadsJensen/dbs_pd

Similar notebooks:

notebook.community | gallery | about