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langouEEG.py
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langouEEG.py
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import os
import numpy as np
import mne
from matplotlib import pyplot as plt
from matplotlib.pyplot import MultipleLocator
import pandas as pd
from mne.time_frequency import tfr_morlet, psd_multitaper, psd_welch
from copy import deepcopy
from mne.preprocessing import create_ecg_epochs, create_eog_epochs, read_ica
import sys
from tensorpac import Pac, EventRelatedPac, PreferredPhase
from tensorpac.utils import PeakLockedTF, PSD, ITC, BinAmplitude
from scipy.integrate import simps
import os.path as op
import matplotlib.pyplot as plt
from scipy.stats import *
from eeg_microstates3 import *
import time
import pickle
from mne.datasets import sample
from mne.datasets import fetch_fsaverage
from mne.viz import plot_topomap
from mpl_toolkits.mplot3d import Axes3D # noqa
import logging
from lempel_ziv_complexity import lempel_ziv_complexity
#mne.utils.set_config('MNE_USE_CUDA', 'true')
dataRoot = "/data/home/viscent/Light"
# dataRoot = sys.path[0]
def init_prog():
global ratio_TD_all_r, ratio_TU_all_r, ratio_DU_all_r
ratio_TD_all_r, ratio_TU_all_r, ratio_DU_all_r = [],[],[]
global ratio_TD_all_f, ratio_TU_all_f, ratio_DU_all_f
ratio_TD_all_f, ratio_TU_all_f, ratio_DU_all_f = [],[],[]
global ratioMA_TD_all_r, ratioMA_TU_all_r, ratioMA_DU_all_r
ratioMA_TD_all_r, ratioMA_TU_all_r, ratioMA_DU_all_r = [],[],[]
global ratioMA_TD_all_f, ratioMA_TU_all_f, ratioMA_DU_all_f
ratioMA_TD_all_f, ratioMA_TU_all_f, ratioMA_DU_all_f = [],[],[]
global rel_power_R, rel_power_F, subj_number_R, subj_number_F, rel_power_R_ran, rel_power_F_ran
rel_power_R, rel_power_F, subj_number_R, subj_number_F, rel_power_R_ran, rel_power_F_ran = [],[],[],[],[],[]
return
def save_ratios(filefolder = dataRoot + '/Light'):
global ratio_TD_all_r, ratio_TU_all_r, ratio_DU_all_r
global ratio_TD_all_f, ratio_TU_all_f, ratio_DU_all_f
global ratioMA_TD_all_r, ratioMA_TU_all_r, ratioMA_DU_all_r
global ratioMA_TD_all_f, ratioMA_TU_all_f, ratioMA_DU_all_f
df_r = pd.DataFrame()
df_r['ratio_TD'] = ratio_TD_all_r
df_r['ratio_TU'] = ratio_TU_all_r
df_r['ratio_DU'] = ratio_DU_all_r
df_r.to_csv(filefolder + '/ratios_rest.csv')
df_f = pd.DataFrame()
df_f['ratio_TD'] = ratio_TD_all_f
df_f['ratio_TU'] = ratio_TU_all_f
df_f['ratio_DU'] = ratio_DU_all_f
df_f.to_csv(filefolder + '/ratios_flicker.csv')
dfMA_r = pd.DataFrame()
dfMA_r['ratio_TD'] = ratioMA_TD_all_r
dfMA_r['ratio_TU'] = ratioMA_TU_all_r
dfMA_r['ratio_DU'] = ratioMA_DU_all_r
dfMA_r.to_csv(filefolder + '/ratiosMA_rest.csv')
dfMA_f = pd.DataFrame()
dfMA_f['ratio_TD'] = ratioMA_TD_all_f
dfMA_f['ratio_TU'] = ratioMA_TU_all_f
dfMA_f['ratio_DU'] = ratioMA_DU_all_f
dfMA_f.to_csv(filefolder + '/ratiosMA_flicker.csv')
return
def csv_transformat(filefolder = dataRoot + '/Light', type='flicker'):
filefolder = dataRoot + '/Light'
df=pd.read_csv(filefolder + '/ratios_{0}.csv'.format(type))
shape = df.shape
print(shape)
rows = shape[0]
column = shape[1]
col_names = df.columns.values.tolist()
ratios = []
labels = []
print(col_names)
for i in range(1, column):
ratio = df.iloc[0:rows,i].tolist()
type_label = [col_names[i]] * len(ratio)
ratios += ratio
labels += type_label
save = pd.DataFrame()
save['ratios'] = ratios
save['labels'] = labels
save.to_csv(filefolder + '/ratios_{0}_all.csv'.format(type))
print('{0} CSV transformation complete'.format(type))
return
def csv_transformat_MA(filefolder = dataRoot + '/Light', type='flicker'):
filefolder = dataRoot + '/Light'
df=pd.read_csv(filefolder + '/ratiosMA_{0}.csv'.format(type))
shape = df.shape
print(shape)
rows = shape[0]
column = shape[1]
col_names = df.columns.values.tolist()
ratios = []
labels = []
print(col_names)
for i in range(1, column):
ratio = df.iloc[0:rows,i].tolist()
type_label = [col_names[i]] * len(ratio)
ratios += ratio
labels += type_label
save = pd.DataFrame()
save['ratios'] = ratios
save['labels'] = labels
save.to_csv(filefolder + '/ratiosMA_{0}_all.csv'.format(type))
print('{0} CSV transformation complete'.format(type))
return
def initData(subject_name,picks_str=['O1','O2','OZ']):
file_path = dataRoot+'/Light/'+subject_name + " Data.cnt"
print(dataRoot)
print(file_path)
raw = mne.io.read_raw_cnt(file_path, preload=True)
# picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=True, exclude='bads')
picks = mne.pick_channels(raw.info["ch_names"], picks_str)
raw = raw.set_channel_types({'Trigger':'stim','VEO':'eog'})
if not os.path.exists(dataRoot+('/Light')):
os.mkdir('Light')
return raw,picks,picks_str
def initData_clean(subject_name,picks_str=['O1','O2','OZ']):
file_path = dataRoot+'/Light/'+subject_name + " Data.cnt"
print(dataRoot)
print(file_path)
raw = mne.io.read_raw_cnt(file_path, preload=True)
# picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=True, exclude='bads')
picks = picks_str
if not os.path.exists(dataRoot+('/Light')):
os.mkdir('Light')
return raw,picks,picks_str
def initLayout(raw):
# layout = pd.read_csv(dataRoot + '/channel_dict.txt', sep = '\t')
# layout.columns = layout.columns.str.strip()
# layout["labels"] = layout["labels"].str.strip()
# layout = layout.set_index('labels')
# layout = layout.to_dict(orient = "index")
# for channel in layout.keys():
# yxz = np.array([layout[channel]["Y"], layout[channel]["X"], layout[channel]["Z"]])
# layout[channel] = yxz
# layout = mne.channels.make_dig_montage(layout, coord_frame='head')
# mne.viz.plot_montage(layout)
# raw.set_montage(layout)
quikMontage = mne.channels.read_custom_montage(os.path.join(dataRoot,'LangouEEG','quikCap.elc'))
raw = raw.copy().set_montage(quikMontage)
return raw
def extractEvents(raw):
# cnt file describe
# print("file info:")
# print(raw.info)
# print("channel names:")
# print(raw.info["ch_names"])
# print("time period:")
# print(raw.n_times)
# #print("time points:")
# #print(raw.times)
# print("events:")
events, event_dict = mne.events_from_annotations(raw)
# event_dict = {'random_flicker-60s':1, 'random_rest-300s':2, '40Hz_rest-300s':3, '40Hz_flicker-60s':4}
print(event_dict)
return events, event_dict
def filterRaw(raw,picks, ref_set_average=False, ref_channels=['M1', 'M2']):
raw = raw.filter(0.1, None, fir_design='firwin')
if ref_set_average:
# 可以考虑使用所有通道的平均值作为参考
raw = raw.copy().set_eeg_reference(ref_channels='average', projection=True)
else:
# 使用特定的参考电极
raw = raw.copy().set_eeg_reference(ref_channels=ref_channels, projection=True)
raw = raw.apply_proj()
raw = raw.notch_filter(freqs=50,method='spectrum_fit')
# raw.plot_psd(area_mode='range', tmax=10.0, picks=picks, average=False)
return raw
def dbgPlot(raw):
# Plot raw data
img_raw_psd = raw.plot_psd()
# Print
scale = dict(mag=1e-12, grad=4e-11, eeg=128e-6, eog=150e-6, ecg=5e-4,
emg=1e-3, ref_meg=1e-12, misc=1e-3, stim=1,
resp=1, chpi=1e-4, whitened=1e2)
# Set fig size
img_raw_plot = raw.plot(duration = 40, n_channels=65, scalings=scale,start=288)
img_raw_plot.set_size_inches([20,20])
# img_raw_plot.savefig(dataRoot + '/img_raw_plot3.png', dpi=300)
def runICA(raw):
# set up and fit the ICA
ica = mne.preprocessing.ICA(n_components=20, random_state=0)
ica.fit(raw)
# ica.plot_components()
bad_ica = ica.detect_artifacts(raw).exclude
raw = ica.apply(raw.copy(), exclude=bad_ica)
return raw
def extractEpochsBlind(raw,events,picks,tmin_rest = -20,tmax_rest = -10,tmin_flick = 3,tmax_flick = 30):
# Get epoch for each event
custom_event_ids = {'LB':5, 'RB':7,'4F':9,'RF':12,'4R':8,'RR':11}
events, event_dict = mne.events_from_annotations(raw)
print(event_dict)
tmin_rest = tmin_rest
tmax_rest = tmax_rest
tmin_flick = tmin_flick
tmax_flick = tmax_flick
reject=dict()
## Epoch: Random flicker
event_id = custom_event_ids['RB']
event_id = event_dict[str(event_id)]
tmin = tmin_flick
tmax = tmax_flick
epoch_RF = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks,reject=reject, baseline=(tmin_flick, tmin_flick), preload=True)
evoked_RF = epoch_RF.average()
#evoked_RF.plot(time_unit='s')
## Epoch: Random rest
event_id = custom_event_ids['RB']
event_id = event_dict[str(event_id)]
tmin = tmin_rest
tmax = tmax_rest
epoch_RR = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks,reject=reject, baseline=(tmin_rest, tmin_rest), preload=True)
evoked_RR = epoch_RR.average()
#evoked_RR.plot(time_unit='s')
## Epoch: 40 Hz rest
event_id = custom_event_ids['LB']
event_id = event_dict[str(event_id)]
tmin = tmin_rest
tmax = tmax_rest
epoch_4R = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks,reject=reject, baseline=(tmin_rest, tmin_rest), preload=True)
#epoch_4R.drop([0,1])
evoked_4R = epoch_4R.average()
#evoked_4R.plot(time_unit='s')
## Epoch: 40 Hz flicker
event_id = custom_event_ids['LB']
event_id = event_dict[str(event_id)]
tmin = tmin_flick
tmax = tmax_flick
epoch_4F = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks,reject=reject,baseline=(tmin_flick, tmin_flick), preload=True)
epoch_4F_all = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
baseline=(tmin_flick, tmin_flick), preload=True,
reject=dict())
#epoch_4F.drop([0,1])
#epoch_4F_all.drop([0,1])
evoked_4F = epoch_4F.average()
#evoked_4F.plot(time_unit='s')
return epoch_RR,epoch_RF,epoch_4R,epoch_4F
def extractEpochs(raw,events,picks,tmin_rest = -30,tmax_rest = -20,tmin_flick = 10,tmax_flick = 20):
# Get epoch for each event
custom_event_ids = {'LB':5, 'RB':7,'4F':9,'RF':12,'4R':8,'RR':11}
tmin_rest = tmin_rest
tmax_rest = tmax_rest
tmin_flick = tmin_flick
tmax_flick = tmax_flick
reject=dict()
## Epoch: Random flicker
events, event_dict = mne.events_from_annotations(raw)
event_id = event_dict[str(custom_event_ids['RF'])]
tmin = tmin_flick
tmax = tmax_flick
epoch_RF = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks,reject=reject, baseline=(tmin, tmax), preload=True)
evoked_RF = epoch_RF.average()
#evoked_RF.plot(time_unit='s')
## Epoch: Random rest
event_id = event_dict[str(custom_event_ids['RF'])]
tmin = tmin_rest
tmax = tmax_rest
epoch_RR = mne.Epochs(raw, events, event_id, tmin_rest, tmax_rest, proj=True,
picks=picks,reject=reject, baseline=None, preload=True)
evoked_RR = epoch_RR.average()
#evoked_RR.plot(time_unit='s')
## Epoch: 40 Hz rest
event_id = event_dict[str(custom_event_ids['4F'])]
tmin = tmin_rest
tmax = tmax_rest
epoch_4R = mne.Epochs(raw, events, event_id, tmin_rest, tmax_rest, proj=True,
picks=picks,reject=reject, baseline=None, preload=True)
#epoch_4R.drop([0,1])
evoked_4R = epoch_4R.average()
#evoked_4R.plot(time_unit='s')
## Epoch: 40 Hz flicker
event_id = event_dict[str(custom_event_ids['4F'])]
tmin = tmin_flick
tmax = tmax_flick
epoch_4F = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks,reject=reject,baseline=(tmin_flick, tmin_flick), preload=True)
epoch_4F_all = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
baseline=(tmin_flick, tmin_flick), preload=True,
reject=dict())
#epoch_4F.drop([0,1])
#epoch_4F_all.drop([0,1])
evoked_4F = epoch_4F.average()
#evoked_4F.plot(time_unit='s')
return epoch_RR,epoch_RF,epoch_4R,epoch_4F
def extractEpochs_id(raw,events,picks,tmin_rest = 60,tmax_rest = 120,tmin_flick = 10,tmax_flick = 20):
# Get epoch for each event
custom_event_ids = {'LB':5, 'RB':7,'4F':9,'RF':12,'4R':8,'RR':11}
tmin_rest = tmin_rest
tmax_rest = tmax_rest
tmin_flick = tmin_flick
tmax_flick = tmax_flick
reject=dict()
## Epoch: Random flicker
events, event_dict = mne.events_from_annotations(raw)
event_id = event_dict[str(custom_event_ids['RF'])]
tmin = tmin_flick
tmax = tmax_flick
epoch_RF = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks,reject=reject, baseline=(tmin_flick, tmin_flick), preload=True)
evoked_RF = epoch_RF.average()
#evoked_RF.plot(time_unit='s')
## Epoch: Random rest
event_id = event_dict[str(custom_event_ids['RR'])]
tmin = tmin_rest
tmax = tmax_rest
epoch_RR = mne.Epochs(raw, events, event_id, tmin_rest, tmax_rest, proj=True,
picks=picks,reject=reject, baseline=None, preload=True)
evoked_RR = epoch_RR.average()
#evoked_RR.plot(time_unit='s')
## Epoch: 40 Hz rest
event_id = event_dict[str(custom_event_ids['4R'])]
tmin = tmin_rest
tmax = tmax_rest
epoch_4R = mne.Epochs(raw, events, event_id, tmin_rest, tmax_rest, proj=True,
picks=picks,reject=reject, baseline=None, preload=True)
#epoch_4R.drop([0,1])
evoked_4R = epoch_4R.average()
#evoked_4R.plot(time_unit='s')
## Epoch: 40 Hz flicker
event_id = event_dict[str(custom_event_ids['4F'])]
tmin = tmin_flick
tmax = tmax_flick
epoch_4F = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks,reject=reject,baseline=(tmin_flick, tmin_flick), preload=True)
epoch_4F_all = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
baseline=(tmin_flick, tmin_flick), preload=True,
reject=dict())
#epoch_4F.drop([0,1])
#epoch_4F_all.drop([0,1])
evoked_4F = epoch_4F.average()
#evoked_4F.plot(time_unit='s')
return epoch_RR,epoch_RF,epoch_4R,epoch_4F
def extractEpochs_forall(raw,events,picks,tmin_rest = 60,tmax_rest = 120,tmin_flick = 10,tmax_flick = 20):
# Get epoch for each event
custom_event_ids = {'LB':5, 'RB':7,'4F':9,'RF':12,'4R':8,'RR':11}
tmin_rest = tmin_rest
tmax_rest = tmax_rest
tmin_flick = tmin_flick
tmax_flick = tmax_flick
reject=dict()
## Epoch: Random flicker
events, event_dict = mne.events_from_annotations(raw)
event_id = event_dict[str(custom_event_ids['RF'])]
tmin = tmin_flick
tmax = tmax_flick
epoch_RF = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks,reject=reject, baseline=None, preload=True)
evoked_RF = epoch_RF.average()
#evoked_RF.plot(time_unit='s')
## Epoch: Random rest
event_id = event_dict[str(custom_event_ids['RR'])]
tmin = tmin_rest
tmax = tmax_rest
epoch_RR = mne.Epochs(raw, events, event_id, tmin_rest, tmax_rest, proj=True,
picks=picks,reject=reject, baseline=None, preload=True)
evoked_RR = epoch_RR.average()
#evoked_RR.plot(time_unit='s')
## Epoch: 40 Hz rest
event_id = event_dict[str(custom_event_ids['4R'])]
tmin = tmin_rest
tmax = tmax_rest
epoch_4R = mne.Epochs(raw, events, event_id, tmin_rest, tmax_rest, proj=True,
picks=picks,reject=reject, baseline=None, preload=True)
#epoch_4R.drop([0,1])
evoked_4R = epoch_4R.average()
#evoked_4R.plot(time_unit='s')
## Epoch: 40 Hz flicker
event_id = event_dict[str(custom_event_ids['4F'])]
tmin = tmin_flick
tmax = tmax_flick
epoch_4F = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks,reject=reject,baseline=None, preload=True)
epoch_4F_all = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
baseline=None, preload=True,
reject=dict())
#epoch_4F.drop([0,1])
#epoch_4F_all.drop([0,1])
evoked_4F = epoch_4F.average()
#evoked_4F.plot(time_unit='s')
return epoch_RR,epoch_RF,epoch_4R,epoch_4F
def doMA(psds,n=20):
for i in range(psds.shape[0]):
tempSum=0
for j in range(int(n)):
if j+i < psds.shape[0]:
tempSum+=psds[j+i]
else:
n=j
break
psds[i]=tempSum/n
return psds
def doMA3D(psds):
for i in range(psds.shape[0]):
for j in range(psds.shape[1]):
psds[i][j] = doMA(psds[i][j])
return psds
def snr_spectrum(psd, noise_n_neighbor_freqs=1, noise_skip_neighbor_freqs=1):
"""Compute SNR spectrum from PSD spectrum using convolution.
Parameters
----------
psd : ndarray, shape ([n_trials, n_channels,] n_frequency_bins)
Data object containing PSD values. Works with arrays as produced by
MNE's PSD functions or channel/trial subsets.
noise_n_neighbor_freqs : int
Number of neighboring frequencies used to compute noise level.
increment by one to add one frequency bin ON BOTH SIDES
noise_skip_neighbor_freqs : int
set this >=1 if you want to exclude the immediately neighboring
frequency bins in noise level calculation
Returns
-------
snr : ndarray, shape ([n_trials, n_channels,] n_frequency_bins)
Array containing SNR for all epochs, channels, frequency bins.
NaN for frequencies on the edges, that do not have enough neighbors on
one side to calculate SNR.
"""
# Construct a kernel that calculates the mean of the neighboring
# frequencies
averaging_kernel = np.concatenate((
np.ones(noise_n_neighbor_freqs),
np.zeros(2 * noise_skip_neighbor_freqs + 1),
np.ones(noise_n_neighbor_freqs)))
averaging_kernel /= averaging_kernel.sum()
# Calculate the mean of the neighboring frequencies by convolving with the
# averaging kernel.
mean_noise = np.apply_along_axis(
lambda psd_: np.convolve(psd_, averaging_kernel, mode='valid'),
axis=-1, arr=psd
)
# The mean is not defined on the edges so we will pad it with nas. The
# padding needs to be done for the last dimension only so we set it to
# (0, 0) for the other ones.
edge_width = noise_n_neighbor_freqs + noise_skip_neighbor_freqs
pad_width = [(0, 0)] * (mean_noise.ndim - 1) + [(edge_width, edge_width)]
mean_noise = np.pad(
mean_noise, pad_width=pad_width, constant_values=np.nan
)
return psd / mean_noise
def add_arrows(axes):
# add some arrows at 60 Hz and its harmonics
for ax in axes:
freqs = ax.lines[-1].get_xdata()
psds = ax.lines[-1].get_ydata()
freq = 40
idx = np.searchsorted(freqs, freq)
# get ymax of a small region around the freq. of interest
y = psds[(idx - 4):(idx + 5)].max()
ax.arrow(x=freqs[idx], y=y + 18, dx=0, dy=-12, color='red',
width=0.1, head_width=3, length_includes_head=True)
def specPlot(epochs):
# MA: 是否做滑动平均
for epoch in epochs:
epoch.plot_psd(fmin=0.1, fmax=100., average=True, spatial_colors=False)
def plot_psd_sub(epoch,ax, fmin=.1, fmax=100, n_jobs=8, color='k', alpha=.5, label='Default', isma=False):
psds, freqs = psd_multitaper(epoch, fmin=fmin, fmax=fmax, n_jobs=n_jobs)
# MA: 是否做滑动平均
if isma:
psds = doMA3D(psds)
psds = 10. * np.log10(psds)
psds_mean = psds.mean(0).mean(0)
psds_std = psds.mean(0).std(0)
ax.plot(freqs, psds_mean, color=color, label = label)
ax.fill_between(freqs, psds_mean - psds_std, psds_mean + psds_std,
color=color, alpha=alpha)
ax.legend()
def superposGamma(epoch_4R,epoch_4F,epoch_RF,subject_name,MA=False):
fmin = 35
fmax = 45
alpha = .2
f, ax = plt.subplots(figsize=(15,7))
if MA:
plot_psd_sub(ax=ax,epoch = epoch_4R, color='y', fmin=fmin, fmax=fmax, alpha=alpha, label='Rest State', isma=True)
plot_psd_sub(ax=ax,epoch = epoch_4F, color='k', fmin=fmin, fmax=fmax, alpha=alpha, label='40 Hz Light Stimulation', isma=True)
plot_psd_sub(ax=ax,epoch = epoch_RF, color='r', fmin=fmin, fmax=fmax, alpha=alpha, label='Random Hz Light Stimulation', isma=True)
else:
plot_psd_sub(ax=ax,epoch = epoch_4R, color='y', fmin=fmin, fmax=fmax, alpha=alpha, label='Rest State')
plot_psd_sub(ax=ax,epoch = epoch_4F, color='k', fmin=fmin, fmax=fmax, alpha=alpha, label='40 Hz Light Stimulation')
plot_psd_sub(ax=ax,epoch = epoch_RF, color='r', fmin=fmin, fmax=fmax, alpha=alpha, label='Random Hz Light Stimulation')
plt.xlabel("Frequency")
plt.ylabel("Power spectral density (PSD) in log")
plt.savefig(dataRoot + '/Light/Light_figures_non/' + subject_name + '_35_45.png')
def superposFull(epoch_4R,epoch_4F,epoch_RF,subject_name,MA=False):
fmin = 0
fmax = 120
alpha = .2
f, ax = plt.subplots(figsize=(15,7))
if MA:
plot_psd_sub(ax=ax,epoch = epoch_4R, color='y', fmin=fmin, fmax=fmax, alpha=alpha, label='Rest State', isma=True)
plot_psd_sub(ax=ax,epoch = epoch_4F, color='k', fmin=fmin, fmax=fmax, alpha=alpha, label='40 Hz Light Stimulation', isma=True)
plot_psd_sub(ax=ax,epoch = epoch_RF, color='r', fmin=fmin, fmax=fmax, alpha=alpha, label='Random Hz Light Stimulation', isma=True)
else:
plot_psd_sub(ax=ax,epoch = epoch_4R, color='y', fmin=fmin, fmax=fmax, alpha=alpha, label='Rest State')
plot_psd_sub(ax=ax,epoch = epoch_4F, color='k', fmin=fmin, fmax=fmax, alpha=alpha, label='40 Hz Light Stimulation')
plot_psd_sub(ax=ax,epoch = epoch_RF, color='r', fmin=fmin, fmax=fmax, alpha=alpha, label='Random Hz Light Stimulation')
plt.xlabel("Frequency")
plt.ylabel("Power spectral density (PSD) in log")
plt.savefig(dataRoot + '/Light/Light_figures_non/' + subject_name + '_0_120.png')
def superpos85(epoch_4R,epoch_4F,epoch_RF,subject_name,MA=False):
fmin = 40
fmax = 85
alpha = .2
f, ax = plt.subplots(figsize=(15,7))
if MA:
plot_psd_sub(ax=ax,epoch = epoch_4R, color='y', fmin=fmin, fmax=fmax, alpha=alpha, label='Rest State', isma=True)
plot_psd_sub(ax=ax,epoch = epoch_4F, color='k', fmin=fmin, fmax=fmax, alpha=alpha, label='40 Hz Light Stimulation', isma=True)
plot_psd_sub(ax=ax,epoch = epoch_RF, color='r', fmin=fmin, fmax=fmax, alpha=alpha, label='Random Hz Light Stimulation', isma=True)
else:
plot_psd_sub(ax=ax,epoch = epoch_4R, color='y', fmin=fmin, fmax=fmax, alpha=alpha, label='Rest State')
plot_psd_sub(ax=ax,epoch = epoch_4F, color='k', fmin=fmin, fmax=fmax, alpha=alpha, label='40 Hz Light Stimulation')
plot_psd_sub(ax=ax,epoch = epoch_RF, color='r', fmin=fmin, fmax=fmax, alpha=alpha, label='Random Hz Light Stimulation')
plt.xlabel("Frequency")
plt.ylabel("Power spectral density (PSD) in log")
plt.savefig(dataRoot + '/Light/Light_figures_non/' + subject_name + '_40_85.png')
def getRatio_rest(epoch,fmin=35.0,fmax=45.0,picks=['O1', 'OZ', 'O2']):
psds, freqs = psd_multitaper(epoch,fmin=fmin, fmax=fmax, n_jobs=8,picks=picks)
# psd.shape: (number of epoch, number of channel, frequency)
f_down = 35.0
f_low = 39.0
f_high = 41.0
f_upstream = 45.0
print(psds.shape)
num_of_epoch = psds.shape[0]
num_of_channel = psds[0].shape[0]
print("{0} epochs in total".format(num_of_epoch))
print("{0} channels in total".format(num_of_channel))
print(psds.shape)
# average all channels
psds = np.mean(psds, axis=1)
# extract power in selected frequency bands
downstream_mean_power, target_mean_power, upstream_mean_power = [],[],[]
for i in range(0, num_of_epoch):
a,b,c = [],[],[]
for j in range(0, len(freqs)):
freq = freqs[j]
power = psds[i][j]
if freq < f_low and freq > f_down:
a.append(power)
if freq < f_high and freq > f_low:
b.append(power)
if freq < f_upstream and freq > f_high:
c.append(power)
downstream_mean_power.append(np.max(a))
target_mean_power.append(np.max(b))
upstream_mean_power.append(np.max(c))
print(r'The downstream mean power is:')
print(downstream_mean_power)
print(r'The taget band mean power is:')
print(target_mean_power)
print(r'The upstream mean power is:')
print(upstream_mean_power)
ratio_TD = []
ratio_TU = []
ratio_DU = []
for i in range(0, len(target_mean_power)):
TD = target_mean_power[i]/downstream_mean_power[i]
TU = target_mean_power[i]/upstream_mean_power[i]
DU = downstream_mean_power[i]/upstream_mean_power[i]
ratio_TD.append(TD)
global ratio_TD_all_r
ratio_TD_all_r.append(TD)
ratio_TU.append(TU)
global ratio_TU_all_r
ratio_TU_all_r.append(TU)
ratio_DU.append(DU)
global ratio_DU_all_r
ratio_DU_all_r.append(DU)
print(r'The target/downstream is:')
print(ratio_TD)
print(r'The target/upstream is:')
print(ratio_TU)
print(r'The downstream/upstream is:')
print(ratio_DU)
return downstream_mean_power,target_mean_power,upstream_mean_power
def getRatio_flicker(epoch,fmin=35.0,fmax=45.0,picks=['O1', 'OZ', 'O2']):
psds, freqs = psd_multitaper(epoch,fmin=fmin, fmax=fmax, n_jobs=8,picks=picks)
# psd.shape: (number of epoch, number of channel, frequency)
f_down = 35.0
f_low = 39.0
f_high = 41.0
f_upstream = 45.0
print(psds.shape)
num_of_epoch = psds.shape[0]
num_of_channel = psds[0].shape[0]
print("{0} epochs in total".format(num_of_epoch))
print("{0} channels in total".format(num_of_channel))
print(psds.shape)
# average all channels
psds = np.mean(psds, axis=1)
# extract power in selected frequency bands
downstream_mean_power, target_mean_power, upstream_mean_power = [],[],[]
for i in range(0, num_of_epoch):
a,b,c = [],[],[]
for j in range(0, len(freqs)):
freq = freqs[j]
power = psds[i][j]
if freq < f_low and freq > f_down:
a.append(power)
if freq < f_high and freq > f_low:
b.append(power)
if freq < f_upstream and freq > f_high:
c.append(power)
downstream_mean_power.append(np.max(a))
target_mean_power.append(np.max(b))
upstream_mean_power.append(np.max(c))
print(r'The downstream mean power is:')
print(downstream_mean_power)
print(r'The taget band mean power is:')
print(target_mean_power)
print(r'The upstream mean power is:')
print(upstream_mean_power)
ratio_TD = []
ratio_TU = []
ratio_DU = []
for i in range(0, len(target_mean_power)):
TD = target_mean_power[i]/downstream_mean_power[i]
TU = target_mean_power[i]/upstream_mean_power[i]
DU = downstream_mean_power[i]/upstream_mean_power[i]
ratio_TD.append(TD)
global ratio_TD_all_f
ratio_TD_all_f.append(TD)
ratio_TU.append(TU)
global ratio_TU_all_f
ratio_TU_all_f.append(TU)
ratio_DU.append(DU)
global ratio_DU_all_f
ratio_DU_all_f.append(DU)
print(r'The target/downstream is:')
print(ratio_TD)
print(r'The target/upstream is:')
print(ratio_TU)
print(r'The downstream/upstream is:')
print(ratio_DU)
return downstream_mean_power,target_mean_power,upstream_mean_power
def get_minima(data):
center = np.argmax(data)
print(center)
min_right = data[center]
min_left = data[center]
for i in range(center, data.shape[0]-1):
if data[i+1] <= min_right:
min_right = data[i+1]
else:
break
for i in range(center, 0, -1):
if data[i-1] <= min_left:
min_left = data[i-1]
else:
break
return min_left, min_right
def getRatio_flicker_MA(epoch,fmin=38.0,fmax=42.0,picks=['O1', 'OZ', 'O2']):
psds, freqs = psd_multitaper(epoch,fmin=fmin, fmax=fmax, n_jobs=8,picks=picks)
# MA: 做滑动平均
psds = doMA3D(psds)
# psd.shape: (number of epoch, number of channel, frequency)
f_down = 35.0
f_low = 39.0
f_high = 41.0
f_upstream = 45.0
print(psds.shape)
num_of_epoch = psds.shape[0]
num_of_channel = psds[0].shape[0]
print("{0} epochs in total".format(num_of_epoch))
print("{0} channels in total".format(num_of_channel))
print(psds.shape)
# average all channels
psds = np.mean(psds, axis=1)
# extract power in selected frequency bands
downstream_mean_power, target_mean_power, upstream_mean_power = [],[],[]
for i in range(0, num_of_epoch):
a,c = get_minima(psds[i])
b = np.max(psds[i])
downstream_mean_power.append(a)
target_mean_power.append(b)
upstream_mean_power.append(c)
print(r'The downstream min power is:')
print(downstream_mean_power)
print(r'The taget band max power is:')
print(target_mean_power)
print(r'The upstream min power is:')
print(upstream_mean_power)
ratio_TD = []
ratio_TU = []
ratio_DU = []
for i in range(0, len(target_mean_power)):
TD = target_mean_power[i]/downstream_mean_power[i]
TU = target_mean_power[i]/upstream_mean_power[i]
DU = downstream_mean_power[i]/upstream_mean_power[i]
ratio_TD.append(TD)
global ratioMA_TD_all_f
ratioMA_TD_all_f.append(TD)
ratio_TU.append(TU)
global ratioMA_TU_all_f
ratioMA_TU_all_f.append(TU)
ratio_DU.append(DU)
global ratioMA_DU_all_f
ratioMA_DU_all_f.append(DU)
print(r'The target/downstream is:')
print(ratio_TD)
print(r'The target/upstream is:')
print(ratio_TU)
print(r'The downstream/upstream is:')
print(ratio_DU)
return downstream_mean_power,target_mean_power,upstream_mean_power
def getRatio_rest_MA(epoch,fmin=38.0,fmax=42.0,picks=['O1', 'OZ', 'O2']):
psds, freqs = psd_multitaper(epoch,fmin=fmin, fmax=fmax, n_jobs=8,picks=picks)
# MA: 做滑动平均
psds = doMA3D(psds)
# psd.shape: (number of epoch, number of channel, frequency)
f_down = 35.0
f_low = 39.0
f_high = 41.0
f_upstream = 45.0
print(psds.shape)
num_of_epoch = psds.shape[0]
num_of_channel = psds[0].shape[0]
print("{0} epochs in total".format(num_of_epoch))
print("{0} channels in total".format(num_of_channel))
print(psds.shape)
# average all channels
psds = np.mean(psds, axis=1)
# extract power in selected frequency bands
downstream_mean_power, target_mean_power, upstream_mean_power = [],[],[]
for i in range(0, num_of_epoch):
a,c = get_minima(psds[i])
b = np.max(psds[i])
downstream_mean_power.append(a)
target_mean_power.append(b)
upstream_mean_power.append(c)
print(r'The downstream min power is:')
print(downstream_mean_power)
print(r'The taget band max power is:')
print(target_mean_power)
print(r'The upstream min power is:')
print(upstream_mean_power)
ratio_TD = []
ratio_TU = []
ratio_DU = []
for i in range(0, len(target_mean_power)):
TD = target_mean_power[i]/downstream_mean_power[i]
TU = target_mean_power[i]/upstream_mean_power[i]
DU = downstream_mean_power[i]/upstream_mean_power[i]
ratio_TD.append(TD)
global ratioMA_TD_all_r
ratioMA_TD_all_r.append(TD)
ratio_TU.append(TU)
global ratioMA_TU_all_r
ratioMA_TU_all_r.append(TU)
ratio_DU.append(DU)
global ratioMA_DU_all_r
ratioMA_DU_all_r.append(DU)
print(r'The target/downstream is:')
print(ratio_TD)
print(r'The target/upstream is:')
print(ratio_TU)
print(r'The downstream/upstream is:')
print(ratio_DU)
return downstream_mean_power,target_mean_power,upstream_mean_power
def calc_abs_power_simp(epoch, fmin, fmax, fbottom, ftop, sfreq):
epoch = np.asarray(epoch)
band_power_all = []
# psds, freqs = mne.time_frequency.psd_array_multitaper(epoch, n_jobs=8, sfreq=500.0)
psds, freqs = mne.time_frequency.psd_array_multitaper(epoch, fmin=fbottom, fmax=ftop, n_jobs=8, sfreq=500.0)
# Average all channels
psds = np.mean(psds, axis=1)
# Resolution of calculation
freq_res = freqs[1] - freqs[0]
# Select frequency band
band = [fmin, fmax]
idx_band = ((freqs >= band[0]) & (freqs <= band[1]))
# print(freqs.shape)
# print(psds.shape)
# integrate the PSD
num = (fmax-fmin)/freq_res
for i in range(psds.shape[0]):
band_power = simps(psds[i][idx_band], dx=freq_res)
band_power_all.append(band_power)
# band_power_all.append(band_power/(num))
# print(band)
# print(band_power_all)
return band_power_all
def cal_abs_power_allch(epoch, fmin, fmax, fbottom, ftop, sfreq):
epoch = np.asarray(epoch)
band_power_all = []
# psds, freqs = mne.time_frequency.psd_array_multitaper(epoch, n_jobs=8, sfreq=500.0)
psds, freqs = mne.time_frequency.psd_array_multitaper(epoch, fmin=fbottom, fmax=ftop, n_jobs=8, sfreq=500.0)
# Average all channels
psds = np.mean(psds, axis=0)
# Resolution of calculation
freq_res = freqs[1] - freqs[0]
# Select frequency band
band = [fmin, fmax]
idx_band = ((freqs >= band[0]) & (freqs <= band[1]))
# print(freqs.shape)
# print(psds.shape)
# integrate the PSD
num = (fmax-fmin)/freq_res
for i in range(psds.shape[0]):
band_power = simps(psds[i][idx_band], dx=freq_res)
band_power_all.append(band_power)
# band_power_all.append(band_power/(num))
# print(band)
# print(band_power_all)
return band_power_all
def get_all_abs_power(epoch_R, epoch_F, f_bottom = 35.0, f_low = 39.0, f_high = 41.0, f_top = 50.0, sfreq=500.0):
power_all_R = calc_abs_power_simp(epoch=epoch_R, fmin=f_bottom, fmax=f_top, fbottom=f_bottom, ftop=f_top, sfreq=500.0)
power_R = calc_abs_power_simp(epoch=epoch_R, fmin=f_low, fmax=f_high, fbottom=f_bottom, ftop=f_top, sfreq=500.0)
power_all_F = calc_abs_power_simp(epoch=epoch_F, fmin=f_bottom, fmax=f_top, fbottom=f_bottom, ftop=f_top, sfreq=500.0)
power_F = calc_abs_power_simp(epoch=epoch_F, fmin=f_low, fmax=f_high, fbottom=f_bottom, ftop=f_top, sfreq=500.0)
return power_all_R, power_R, power_all_F, power_F
def get_allch_abs_power(epoch_R, epoch_F, f_bottom = 35.0, f_low = 39.0, f_high = 41.0, f_top = 50.0, sfreq=500.0):
power_all_R = cal_abs_power_allch(epoch=epoch_R, fmin=f_bottom, fmax=f_top, fbottom=f_bottom, ftop=f_top, sfreq=500.0)
power_R = cal_abs_power_allch(epoch=epoch_R, fmin=f_low, fmax=f_high, fbottom=f_bottom, ftop=f_top, sfreq=500.0)
power_all_F = cal_abs_power_allch(epoch=epoch_F, fmin=f_bottom, fmax=f_top, fbottom=f_bottom, ftop=f_top, sfreq=500.0)
power_F = cal_abs_power_allch(epoch=epoch_F, fmin=f_low, fmax=f_high, fbottom=f_bottom, ftop=f_top, sfreq=500.0)
return power_all_R, power_R, power_all_F, power_F
def cal_rel_power_R(power_target, power_all):
global rel_power_R
for i in range(min(len(power_target), len(power_all))):
rel_power_R.append(power_target[i]/power_all[i])
# print(rel_power_R)
return rel_power_R, min(len(power_target), len(power_all))
def cal_rel_power_F(power_target, power_all):
global rel_power_F
for i in range(min(len(power_target), len(power_all))):
rel_power_F.append(power_target[i]/power_all[i])
# print(rel_power_F)
return rel_power_F, min(len(power_target), len(power_all))
def cal_rel_power_R_ran(power_target, power_all):
global rel_power_R_ran
for i in range(min(len(power_target), len(power_all))):
rel_power_R_ran.append(power_target[i]/power_all[i])
# print(rel_power_R_ran)
return rel_power_R_ran, min(len(power_target), len(power_all))
def cal_rel_power_F_ran(power_target, power_all):
global rel_power_F_ran
for i in range(min(len(power_target), len(power_all))):
rel_power_F_ran.append(power_target[i]/power_all[i])
# print(rel_power_F_ran)
return rel_power_F_ran, min(len(power_target), len(power_all))
def cal_rel_power(power_target, power_all):
rel_powers = []
for i in range(min(len(power_target), len(power_all))):
rel_powers.append(power_target[i]/power_all[i])
return rel_powers
def save_rel_powers_subj(length_R, length_F, filefolder = dataRoot + '/Light/csvs', subject_name="Undefined"):
# 添加样本序号索引
global rel_power_R, rel_power_F, subj_number_R, subj_number_F
subj_number_R = subj_number_R + list([subject_name])*length_R
subj_number_F = subj_number_F + list([subject_name])*length_F
df_R, df_F,df_subj_R, df_subj_F = pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
df_R['rel_power_rest'] = rel_power_R
df_F['rel_power_flicker'] = rel_power_F
df_subj_R["Subject Number Rest"] = subj_number_R
df_subj_F["Subject Number Flicker"] = subj_number_F
df_csv = pd.concat([df_R, df_subj_R, df_F, df_subj_F], axis=1)
df_csv.to_csv(filefolder + '/rel_powers.csv')
def save_rel_powers(filefolder = dataRoot + '/Light/csvs'):
df_R, df_F= pd.DataFrame(), pd.DataFrame()
df_R['rel_power_rest'] = rel_power_R
df_F['rel_power_flicker'] = rel_power_F
df_csv = pd.concat([df_R, df_F], axis=1)
df_csv.to_csv(filefolder + '/rel_powers.csv')
def calc_psds(epoch, fmin, fmax, n_jobs=1, type='multitaper'):
if type=='multitaper':
psds, freqs = psd_multitaper(epoch, fmin=fmin, fmax=fmax, n_jobs=n_jobs)
return psds, freqs
return
def save_psd(psd, freqs, filepath='default.csv', pad=5):
# psd = np.mean(psd, axis=1）
psd = psd[:, ::pad]
freqs = freqs[::pad]
psd = np.insert(psd, 0, values=freqs, axis=0)
np.savetxt(filepath, psd, delimiter=',')
return
'''
For microstate analysis
'''
def plot_substate(epoch, maps, n_maps, dpi=300, save=False, filename='Default', fmt='.png', result_dir=''):
fig, axis = plt.subplots(1, n_maps, dpi=dpi)
for i in range(0,n_maps):
axis[i].set_title('state{0}'.format(i))
# axis[i].set_title('state{0}'.format(i+1))
plot_topomap(maps[i], epoch.info, axes=axis[i], show=False)
fig.show()
if save:
fig.savefig(result_dir + '/' + filename + fmt)
return fig
def save_sub_stateplots(epoch, maps, n_maps, dpi=300, save=False, filename='Default', fmt='.png', result_dir=''):
result_dir = result_dir + '/' + fmt
if not os.path.exists(result_dir): #判断是否存在文件夹如果不存在则创建为文件夹
os.makedirs(result_dir)
for i in range(0,n_maps):
fig = plt.figure(dpi = dpi)
plot_topomap(maps[i], epoch.info, show=False)
fig.savefig(result_dir + '/' + filename + "_{0}".format(i) + fmt)
fig.clf()
return
def display_gfp_peaks(gfp_peaks, x, fs):
pps = len(gfp_peaks) / (len(x)/fs) # peaks per second
print(f"\nGFP peaks per sec.: {pps:.2f}")
return
def display_gev(gev):
print("\nGlobal explained variance (GEV) per map:")
for i, g in enumerate(gev): print(f"GEV(ms-{i:d}) = {gev[i]:.2f}")
print(f"\ntotal GEV: {gev.sum():.3f}")
return
def display_info(x, n_maps, gfp_peaks, gev, fs, savelog=False, result_dir='', state='Default', tm=''):
p_hat = p_empirical(x, n_maps)
T_hat = T_empirical(x, n_maps)
print("\nEmpirical symbol distribution (RTT):\n")
for i in range(n_maps):
print(f"p_{i:d} = {p_hat[i]:.3f}")
print("\nEmpirical transition matrix:\n")
print_matrix(T_hat)
display_gfp_peaks(gfp_peaks=gfp_peaks,x=x, fs=fs)
display_gev(gev)
h_hat = H_1(x, n_maps)
h_max = max_entropy(n_maps)
print(f"\nEmpirical entropy H = {h_hat:.2f} (max. entropy: {h_max:.2f})")
h_rate, _ = excess_entropy_rate(x, n_maps, kmax=8, doplot=True)
h_mc = mc_entropy_rate(p_hat, T_hat)
print(f"\nEmpirical entropy rate h = {h_rate:.2f}")
print(f"Theoretical MC entropy rate h = {h_mc:.2f}")
if savelog:
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.DEBUG,
filename=result_dir + '/result_k{0}.log'.format(n_maps),
filemode='a',
# format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s'
format = ''
)
logger.info('\n\n' + tm)
logger.info('\nCondition: {0}'.format(state))
logger.info(f"\ntotal GEV: {gev.sum():.6f}")
logger.info('\nEmpirical symbol distribution (RTT):\n')
for i in range(n_maps):
logger.info(f"p_{i:d} = {p_hat[i]:.8f}")
logger.info("\nEmpirical transition matrix:")
logger.info(np.array2string(T_hat, separator=', '))
return
def display_states(x, pca1):
fig, ax = plt.subplots(2, 1, figsize=(15,4), sharex=True)
ax[0].plot(x[0:3000])
ax[1].plot(pca1[0:3000])
return
def save_to_file(array, filepath):
np.asarray(array)
df = pd.DataFrame (array)
df.to_csv(filepath, index=False)