def spectral_entropy(self, col_data):
"""
Purpose : compute spectral entropy accross the data
Arg:
data : input EEG signals data
window_sze : moving window size
Output:
df : results after computing moving spectral entropy
"""
data = col_data
window_size = self.window_size
df = []
for i in range(int(len(data) / window_size)):
complexity = nk.complexity(data[i * window_size:(i + 1) *
window_size],
spectral=True,
shannon=False,
sampen=False,
multiscale=False,
svd=False,
correlation=False,
higushi=False,
petrosian=False,
fisher=False,
hurst=False,
dfa=False,
lyap_r=False,
lyap_e=False)
df.append(complexity['Entropy_Spectral'])
df = pd.DataFrame(df)
return df.reset_index(drop=True)
def wavelet_transform(self, col_data):
data = col_data
window_size = self.window_size
ca_df = []
cd_df = []
for i in range(int(len(data) / window_size)):
(cA, cD) = pywt.dwt(data[i * window_size:(i + 1) * window_size],
'db4')
ca_ = nk.complexity(cA,
spectral=True,
shannon=False,
sampen=False,
multiscale=False,
svd=False,
correlation=False,
higushi=False,
petrosian=False,
fisher=False,
hurst=False,
dfa=False,
lyap_r=False,
lyap_e=False)
cd_ = nk.complexity(cD,
spectral=True,
shannon=False,
sampen=False,
multiscale=False,
svd=False,
correlation=False,
higushi=False,
petrosian=False,
fisher=False,
hurst=False,
dfa=False,
lyap_r=False,
lyap_e=False)
ca_df.append(ca_['Entropy_Spectral'])
cd_df.append(cd_['Entropy_Spectral'])
df = pd.DataFrame({'CA': ca_df, 'CD': cd_df})
return df.reset_index(drop=True)
def extract(self, signal):
logging.info("extracting %s" % self.__class__.__name__)
return {
self.__class__.__name__:
neurokit.complexity(signal,
shannon=False,
sampen=False,
multiscale=False,
spectral=False,
svd=False,
correlation=False,
higushi=True,
petrosian=False,
fisher=False,
hurst=False,
dfa=False,
lyap_r=False,
lyap_e=False,
emb_dim=2)['Fractal_Dimension_Higushi']
}
RUN_LIST = {'pareidolia':['1','2','3','4'],
'RS':['1', '2']}
SUBJ_LIST = ['01', '02']
TASK_LIST = ['pareidolia', 'RS']
##Compute complexity
for freq, name in zip(FREQ_BANDS, SPECTRAL_NAMES):
for subj in SUBJ_LIST:
for task in TASK_LIST:
for run in RUN_LIST[task]:
epochs_name, epochs_path = get_pareidolia_bids(FOLDERPATH, subj, task, run, stage = 'epo_FDclass_slowVSfast', cond=None)
epochs = mne.read_epochs(epochs_path)
epochs_data = epochs.get_data()
complex_tot = []
for i in range(len(epochs_data)):
complex_trials = []
for j in range(114):
complexity_eeg = nk.complexity(epochs_data[i, j, :], sampling_rate=epochs.info['sfreq'],
shannon=False, sampen=False, multiscale=False, spectral=True, svd=False,
correlation=False, higushi=False, petrosian=False, fisher=False, hurst=False,
dfa=False, lyap_r=False, lyap_e=False, bands=freq)
#print(complexity_eeg.shape)
complex_trials.append(complexity_eeg)
complex_tot.append(complex_trials)
complex_tot = np.array(complex_tot)
complex_file, complex_path = get_pareidolia_bids(FOLDERPATH, subj, task, run, stage = name)
savemat(complex_path, {name: complex_tot})
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import neurokit as nk
import nolds
df = pd.read_csv('data.csv')
df = nk.ecg_process(ecg=df["ECG"])
rri = df["ECG"]["RR_Intervals"]
signal=rri
tolerance = "default"
emb_dim=2
chaos = nk.complexity(signal, lyap_r=False, lyap_e=False)
#chaos = pd.Series(chaos)
#chaos.index = ["ECG_Complexity_" + s for s in chaos.index]
#processed_ecg["ECG"]["Complexity"] = chaos.to_dict()
#nolds.sampen(rri, 2, 0.1*np.std(rri), dist="euler")
#sample_entropy(rri,2, 0.1*np.std(rri))[1]
#
#
# 2.
# Compute hilbert using brainpipe
f = [ [2, 4], [4, 8], [8, 12], [12, 30], [30, 60] ]
npts = data.shape[1]
print(data.shape)
power_obj = power(sf, npts, f=f, baseline=None, norm=None, method='hilbert', window=None, width=None, step=None, split=None, time=None)
start = time.time()
power_vals = power_obj.get(data.T)
stop = time.time()
print(stop-start)
print(power_vals[0].shape)
print(power_vals[1])
print('Done')
powow = power_vals[0].squeeze()
dtp = powow[2,:,0]
t = np.arange(0,len(dtp),1)
plt.plot(t, dtp)
plt.show()
start = time.time()
comp = neurokit.complexity(dtp)
stop = time.time()
print("duration of computation :")
print(stop-start)
print(comp)
# 3.
# Compute complexity using neurokit
# filename='phase_diagram_{0}_lag={1}'.format(TICKERS[i],LAG))
plot_phase_plot(pops=pops,
filename='phase_diagram_{0}_lags={1}'.format(
TICKERS[i], str(LAGS)),
show=False)
LYAP.append(
nk.complexity(pops.close_1.as_matrix(),
lyap_r=True,
lyap_e=False,
sampling_rate=1000,
shannon=False,
sampen=False,
multiscale=False,
spectral=False,
svd=False,
correlation=True,
higushi=False,
petrosian=False,
fisher=False,
hurst=False,
dfa=False,
emb_dim=2,
tolerance="default",
k_max=8,
bands=None,
tau=1))
print(TICKERS)
print(LYAP)
