CueingGroupAnalysis_Colab¶
This notebook is slightly different than the PC notebooks due to differences in the Mac operating system.
First setup the computer by following the instructions in the mac_instructions_cueing.docx file, then return to this file.
The cueing task can ellicit a number of reliable changes. A central cue indicates the location of an upcoming target onset. Here the task can be changed to be perfectly predictive, or have some level of cue validity. Task is to indicate the orientation of a spatial grating on the target, up for vertical, right for horizontal.
ERP - Validly cued targets ellict larger ERP's than invalidly cued targets
Response ERPs - Validly cued targets are more quickly identified and better identified
Oscillations - Alpha power lateralizes after a spatial cue onset preceeding the upcoming onset of a target. Alpha power becomes smaller contraleral to the target side, and larger ipsilateral with the target.
In [1]:
!git clone https://github.com/kylemath/eeg-notebooks --recurse-submodules
%cd eeg-notebooks/notebooks
fatal: destination path 'eeg-notebooks' already exists and is not an empty directory. /content/eeg-notebooks/notebooks
In [2]:
#from muselsl import stream, list_muses, view, record
!pip install mne
from multiprocessing import Process
from mne import Epochs, find_events, concatenate_raws
from mne.time_frequency import tfr_morlet
import numpy as np
from time import time, strftime, gmtime
import os
#from stimulus_presentation import cueing
from utils import utils
from collections import OrderedDict
import warnings
warnings.filterwarnings('ignore')
from matplotlib import pyplot as plt
import matplotlib.patches as patches
Requirement already satisfied: mne in /usr/local/lib/python3.6/dist-packages (0.17.0)
Prepare the Data for Analysis¶
Once a suitable data set has been collected, it is now time to analyze the data and see if we can identify the cueing effects
Load data into MNE objects¶
MNE is a very powerful Python library for analyzing EEG data. It provides helpful functions for performing key tasks such as filtering EEG data, rejecting artifacts, and grouping EEG data into chunks (epochs).
The first step after loading dependencies is use MNE to read the data we've collected into an MNE Raw object
In [8]:
subs = [101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112,
202, 203, 204, 205, 207, 208, 209, 210, 211,
301, 302, 303, 304, 305, 306, 307, 308, 309]
diff_out = []
Ipsi_out = []
Contra_out = []
Ipsi_spectra_out = []
Contra_spectra_out = []
diff_spectra_out = []
ERSP_diff_out = []
ERSP_Ipsi_out = []
ERSP_Contra_out = []
frequencies = np.linspace(6, 30, 100, endpoint=True)
wave_cycles = 6
# time frequency window for analysis
f_low = 7 # Hz
f_high = 10
f_diff = f_high-f_low
t_low = 0 # s
t_high = 1
t_diff = t_high-t_low
bad_subs= [6, 7, 13, 26]
really_bad_subs = [11, 12, 19]
sub_count = 0
for sub in subs:
print(sub)
sub_count += 1
if (sub_count in really_bad_subs):
rej_thresh_uV = 90
elif (sub_count in bad_subs):
rej_thresh_uV = 90
else:
rej_thresh_uV = 90
rej_thresh = rej_thresh_uV*1e-6
# Load both sessions
raw = utils.load_data('visual/cueing', sfreq=256.,
subject_nb=sub, session_nb=1)
raw.append( utils.load_data('visual/cueing', sfreq=256.,
subject_nb=sub, session_nb=2) )
# Filter Raw Data
raw.filter(1,30, method='iir')
#Select Events
events = find_events(raw)
event_id = {'LeftCue': 1, 'RightCue': 2}
epochs = Epochs(raw, events=events, event_id=event_id,
tmin=-1, tmax=2, baseline=(-1, 0),
reject={'eeg':rej_thresh}, preload=True,
verbose=False, picks=[0, 3])
print('Trials Remaining: ' + str(len(epochs.events)) + '.')
# Compute morlet wavelet
# Left Cue
tfr, itc = tfr_morlet(epochs['LeftCue'], freqs=frequencies,
n_cycles=wave_cycles, return_itc=True)
tfr = tfr.apply_baseline((-1,-.5),mode='mean')
#tfr.plot(picks=[0], mode='logratio',
# title='TP9 - Ipsi');
#tfr.plot(picks=[3], mode='logratio',
# title='TP10 - Contra');
power_Ipsi_TP9 = tfr.data[0,:,:]
power_Contra_TP10 = tfr.data[1,:,:]
# Right Cue
tfr, itc = tfr_morlet(epochs['RightCue'], freqs=frequencies,
n_cycles=wave_cycles, return_itc=True)
tfr = tfr.apply_baseline((-1,-.5),mode='mean')
#tfr.plot(picks=[0], mode='logratio',
# title='TP9 - Contra');
#tfr.plot(picks=[3], mode='logratio',
# title='TP10 - Ipsi');
power_Contra_TP9 = tfr.data[0,:,:]
power_Ipsi_TP10 = tfr.data[1,:,:]
# Plot Differences
%matplotlib inline
times = epochs.times
power_Avg_Ipsi = (power_Ipsi_TP9+power_Ipsi_TP10)/2;
power_Avg_Contra = (power_Contra_TP9+power_Contra_TP10)/2;
power_Avg_Diff = power_Avg_Ipsi-power_Avg_Contra;
#find max to make color range
plot_max = np.max([np.max(np.abs(power_Avg_Ipsi)), np.max(np.abs(power_Avg_Contra))])
plot_diff_max = np.max(np.abs(power_Avg_Diff))
#Ipsi
fig, ax = plt.subplots(1)
im = plt.imshow(power_Avg_Ipsi,
extent=[times[0], times[-1], frequencies[0], frequencies[-1]],
aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)
plt.xlabel('Time (sec)')
plt.ylabel('Frequency (Hz)')
plt.title('Power Average Ipsilateral to Cue')
cb = fig.colorbar(im)
cb.set_label('Power')
# Create a Rectangle patch
rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')
# Add the patch to the Axes
ax.add_patch(rect)
#TP10
fig, ax = plt.subplots(1)
im = plt.imshow(power_Avg_Contra,
extent=[times[0], times[-1], frequencies[0], frequencies[-1]],
aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)
plt.xlabel('Time (sec)')
plt.ylabel('Frequency (Hz)')
plt.title(str(sub) + ' - Power Average Contra to Cue')
cb = fig.colorbar(im)
cb.set_label('Power')
# Create a Rectangle patch
rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')
# Add the patch to the Axes
ax.add_patch(rect)
#difference between conditions
fig, ax = plt.subplots(1)
im = plt.imshow(power_Avg_Diff,
extent=[times[0], times[-1], frequencies[0], frequencies[-1]],
aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_diff_max, vmax=plot_diff_max)
plt.xlabel('Time (sec)')
plt.ylabel('Frequency (Hz)')
plt.title('Power Difference Ipsi-Contra')
cb = fig.colorbar(im)
cb.set_label('Ipsi-Contra Power')
# Create a Rectangle patch
rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')
# Add the patch to the Axes
ax.add_patch(rect)
#output data into array
Ipsi_out.append(np.mean(power_Avg_Ipsi[np.argmax(frequencies>f_low):
np.argmax(frequencies>f_high)-1,
np.argmax(times>t_low):np.argmax(times>t_high)-1 ]
)
)
Ipsi_spectra_out.append(np.mean(power_Avg_Ipsi[:,np.argmax(times>t_low):
np.argmax(times>t_high)-1 ],1
)
)
Contra_out.append(np.mean(power_Avg_Contra[np.argmax(frequencies>f_low):
np.argmax(frequencies>f_high)-1,
np.argmax(times>t_low):np.argmax(times>t_high)-1 ]
)
)
Contra_spectra_out.append(np.mean(power_Avg_Contra[:,np.argmax(times>t_low):
np.argmax(times>t_high)-1 ],1))
diff_out.append(np.mean(power_Avg_Diff[np.argmax(frequencies>f_low):
np.argmax(frequencies>f_high)-1,
np.argmax(times>t_low):np.argmax(times>t_high)-1 ]
)
)
diff_spectra_out.append(np.mean(power_Avg_Diff[:,np.argmax(times>t_low):
np.argmax(times>t_high)-1 ],1
)
)
ERSP_diff_out.append(power_Avg_Diff)
ERSP_Ipsi_out.append(power_Avg_Ipsi)
ERSP_Contra_out.append(power_Avg_Contra)
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Range : 0 ... 61307 = 0.000 ... 239.480 secs
Ready.
Creating RawArray with float64 data, n_channels=5, n_times=61308
Range : 0 ... 61307 = 0.000 ... 239.480 secs
Ready.
Setting up band-pass filter from 1 - 30 Hz
Using filter length: 61308
Setting up band-pass filter from 1 - 30 Hz
Using filter length: 61308
196 events found
Event IDs: [ 1 2 11 12 21 22]
Trials Remaining: 74.
Applying baseline correction (mode: mean)
Applying baseline correction (mode: mean)
Combine Subjects¶
In [4]:
print(np.shape(ERSP_diff_out))
print(np.shape(Contra_spectra_out))
print(diff_out)
GrandAvg_diff = np.nanmean(ERSP_diff_out,0)
GrandAvg_Ipsi = np.nanmean(ERSP_Ipsi_out,0)
GrandAvg_Contra = np.nanmean(ERSP_Contra_out,0)
GrandAvg_spec_Ipsi = np.nanmean(Ipsi_spectra_out,0)
GrandAvg_spec_Contra = np.nanmean(Contra_spectra_out,0)
GrandAvg_spec_diff = np.nanmean(diff_spectra_out,0)
num_good = len(diff_out) - sum(np.isnan(diff_out))
GrandAvg_spec_Ipsi_ste = np.nanstd(Ipsi_spectra_out,0)/np.sqrt(num_good)
GrandAvg_spec_Contra_ste = np.nanstd(Contra_spectra_out,0)/np.sqrt(num_good)
GrandAvg_spec_diff_ste = np.nanstd(diff_spectra_out,0)/np.sqrt(num_good)
#Spectra error bars
fig, ax = plt.subplots(1)
plt.errorbar(frequencies,GrandAvg_spec_Ipsi,yerr=GrandAvg_spec_Ipsi_ste)
plt.errorbar(frequencies,GrandAvg_spec_Contra,yerr=GrandAvg_spec_Contra_ste)
plt.legend(('Ipsi','Contra'))
plt.xlabel('Frequency (Hz)')
plt.ylabel('Power (uV^2)')
plt.hlines(0,3,33)
#Spectra Diff error bars
fig, ax = plt.subplots(1)
plt.errorbar(frequencies,GrandAvg_spec_diff,yerr=GrandAvg_spec_diff_ste)
plt.legend('Ipsi-Contra')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Power (uV^2)')
plt.hlines(0,3,33)
#Grand Average Ipsi
plot_max = np.max([np.max(np.abs(GrandAvg_Ipsi)), np.max(np.abs(GrandAvg_Contra))])
fig, ax = plt.subplots(1)
im = plt.imshow(GrandAvg_Ipsi,
extent=[times[0], times[-1], frequencies[0], frequencies[-1]],
aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)
plt.xlabel('Time (sec)')
plt.ylabel('Frequency (Hz)')
plt.title('Power Ipsi')
cb = fig.colorbar(im)
cb.set_label('Power')
# Create a Rectangle patch
rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')
# Add the patch to the Axes
ax.add_patch(rect)
#Grand Average Contra
fig, ax = plt.subplots(1)
im = plt.imshow(GrandAvg_Contra,
extent=[times[0], times[-1], frequencies[0], frequencies[-1]],
aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)
plt.xlabel('Time (sec)')
plt.ylabel('Frequency (Hz)')
plt.title('Power Contra')
cb = fig.colorbar(im)
cb.set_label('Power')
# Create a Rectangle patch
rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')
# Add the patch to the Axes
ax.add_patch(rect)
#Grand Average Ipsi-Contra Difference
plot_max_diff = np.max(np.abs(GrandAvg_diff))
fig, ax = plt.subplots(1)
im = plt.imshow(GrandAvg_diff,
extent=[times[0], times[-1], frequencies[0], frequencies[-1]],
aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max_diff, vmax=plot_max_diff)
plt.xlabel('Time (sec)')
plt.ylabel('Frequency (Hz)')
plt.title('Power Difference Ipsi-Contra')
cb = fig.colorbar(im)
cb.set_label('Ipsi-Contra Power')
# Create a Rectangle patch
rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')
# Add the patch to the Axes
ax.add_patch(rect)
(29, 100, 769) (29, 100) [-1.8633016529355503e-10, -5.19449269804164e-11, 3.965717366845645e-11, -9.439937924298804e-11, 1.5395426956926097e-10, nan, nan, 1.437520530470469e-10, 6.156413801292706e-12, -3.454228962737695e-11, nan, 1.4040022690241467e-10, 4.5250575579733986e-11, 8.133414421717266e-11, 1.7562617842729205e-12, 3.4989854712143306e-11, -7.35844888841272e-11, -2.0667398674718962e-11, 5.83383223147634e-11, -1.1353507280272532e-10, 2.057854566935435e-10, 8.136129078256774e-11, 2.2311245651295555e-09, 4.417204016506356e-13, 1.5824220701839011e-10, nan, 2.972694444208326e-11, -2.705345319003894e-11, 1.0032262566341921e-10]
Out[4]:
<matplotlib.patches.Rectangle at 0x7fd9b86f23c8>
Compute T-test¶
In [5]:
import scipy
num_good = len(diff_out) - sum(np.isnan(diff_out))
[tstat, pval] = scipy.stats.ttest_ind(diff_out,np.zeros(len(diff_out)),nan_policy='omit')
print('Ipsi Mean: '+ str(np.nanmean(Ipsi_out)))
print('Contra Mean: '+ str(np.nanmean(Contra_out)))
print('Mean Diff: '+ str(np.nanmean(diff_out)))
print('t(' + str(num_good-1) + ') = ' + str(round(tstat,3)))
print('p = ' + str(round(pval,3)))
Ipsi Mean: 9.024561362956465e-11 Contra Mean: -2.6175863611708235e-11 Mean Diff: 1.164214772412728e-10 t(24) = 1.394 p = 0.169
Save average powers ipsi and contra¶
In [6]:
import pandas as pd
print(diff_out)
raw_data = {'Ipsi Power': Ipsi_out,
'Contra Power': Contra_out}
df = pd.DataFrame(raw_data, columns = ['Ipsi Power', 'Contra Power'])
df.to_csv('375CueingEEG.csv')
print('Saved subject averages for each condition to 375CueingEEG.csv file in present directory')
[-1.8633016529355503e-10, -5.19449269804164e-11, 3.965717366845645e-11, -9.439937924298804e-11, 1.5395426956926097e-10, nan, nan, 1.437520530470469e-10, 6.156413801292706e-12, -3.454228962737695e-11, nan, 1.4040022690241467e-10, 4.5250575579733986e-11, 8.133414421717266e-11, 1.7562617842729205e-12, 3.4989854712143306e-11, -7.35844888841272e-11, -2.0667398674718962e-11, 5.83383223147634e-11, -1.1353507280272532e-10, 2.057854566935435e-10, 8.136129078256774e-11, 2.2311245651295555e-09, 4.417204016506356e-13, 1.5824220701839011e-10, nan, 2.972694444208326e-11, -2.705345319003894e-11, 1.0032262566341921e-10] Saved subject averages for each condition to 375CueingEEG.csv file in present directory
Save Spectra¶
In [7]:
df = pd.DataFrame(Ipsi_spectra_out,columns=frequencies)
df.to_csv('375CueingIpsiSpec.csv')
df = pd.DataFrame(Contra_spectra_out,columns=frequencies)
df.to_csv('375CueingContraSpec.csv')
print('Saved Spectra to 375Cueing*Spec.csv file in present directory')
Saved Spectra to 375Cueing*Spec.csv file in present directory