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eeg.py
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eeg.py
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# -*- coding: utf-8 -*-
"""
biosppy.signals.eeg
-------------------
This module provides methods to process Electroencephalographic (EEG)
signals.
:copyright: (c) 2015-2018 by Instituto de Telecomunicacoes
:license: BSD 3-clause, see LICENSE for more details.
"""
# Imports
# compat
from __future__ import absolute_import, division, print_function
from six.moves import range
# 3rd party
import numpy as np
# local
from . import tools as st
from .. import plotting, utils
def eeg(signal=None, sampling_rate=1000., labels=None, show=True):
"""Process raw EEG signals and extract relevant signal features using
default parameters.
Parameters
----------
signal : array
Raw EEG signal matrix; each column is one EEG channel.
sampling_rate : int, float, optional
Sampling frequency (Hz).
labels : list, optional
Channel labels.
show : bool, optional
If True, show a summary plot.
Returns
-------
ts : array
Signal time axis reference (seconds).
filtered : array
Filtered BVP signal.
features_ts : array
Features time axis reference (seconds).
theta : array
Average power in the 4 to 8 Hz frequency band; each column is one EEG
channel.
alpha_low : array
Average power in the 8 to 10 Hz frequency band; each column is one EEG
channel.
alpha_high : array
Average power in the 10 to 13 Hz frequency band; each column is one EEG
channel.
beta : array
Average power in the 13 to 25 Hz frequency band; each column is one EEG
channel.
gamma : array
Average power in the 25 to 40 Hz frequency band; each column is one EEG
channel.
plf_pairs : list
PLF pair indices.
plf : array
PLF matrix; each column is a channel pair.
"""
# check inputs
if signal is None:
raise TypeError("Please specify an input signal.")
# ensure numpy
signal = np.array(signal)
sampling_rate = float(sampling_rate)
nch = signal.shape[1]
if labels is None:
labels = ['Ch. %d' % i for i in range(nch)]
else:
if len(labels) != nch:
raise ValueError(
"Number of channels mismatch between signal matrix and labels.")
# high pass filter
b, a = st.get_filter(ftype='butter',
band='highpass',
order=8,
frequency=4,
sampling_rate=sampling_rate)
aux, _ = st._filter_signal(b, a, signal=signal, check_phase=True, axis=0)
# low pass filter
b, a = st.get_filter(ftype='butter',
band='lowpass',
order=16,
frequency=40,
sampling_rate=sampling_rate)
filtered, _ = st._filter_signal(b, a, signal=aux, check_phase=True, axis=0)
# band power features
out = get_power_features(signal=filtered,
sampling_rate=sampling_rate,
size=0.25,
overlap=0.5)
ts_feat = out['ts']
theta = out['theta']
alpha_low = out['alpha_low']
alpha_high = out['alpha_high']
beta = out['beta']
gamma = out['gamma']
# PLF features
_, plf_pairs, plf = get_plf_features(signal=filtered,
sampling_rate=sampling_rate,
size=0.25,
overlap=0.5)
# get time vectors
length = len(signal)
T = (length - 1) / sampling_rate
ts = np.linspace(0, T, length, endpoint=False)
# plot
if show:
plotting.plot_eeg(ts=ts,
raw=signal,
filtered=filtered,
labels=labels,
features_ts=ts_feat,
theta=theta,
alpha_low=alpha_low,
alpha_high=alpha_high,
beta=beta,
gamma=gamma,
plf_pairs=plf_pairs,
plf=plf,
path=None,
show=True)
# output
args = (ts, filtered, ts_feat, theta, alpha_low, alpha_high, beta, gamma,
plf_pairs, plf)
names = ('ts', 'filtered', 'features_ts', 'theta', 'alpha_low',
'alpha_high', 'beta', 'gamma', 'plf_pairs', 'plf')
return utils.ReturnTuple(args, names)
def car_reference(signal=None):
"""Change signal reference to the Common Average Reference (CAR).
Parameters
----------
signal : array
Input EEG signal matrix; each column is one EEG channel.
Returns
-------
signal : array
Re-referenced EEG signal matrix; each column is one EEG channel.
"""
# check inputs
if signal is None:
raise TypeError("Please specify an input signal.")
length, nch = signal.shape
avg = np.mean(signal, axis=1)
out = signal - np.tile(avg.reshape((length, 1)), nch)
return utils.ReturnTuple((out,), ('signal',))
def get_power_features(signal=None,
sampling_rate=1000.,
size=0.25,
overlap=0.5):
"""Extract band power features from EEG signals.
Computes the average signal power, with overlapping windows, in typical
EEG frequency bands:
* Theta: from 4 to 8 Hz,
* Lower Alpha: from 8 to 10 Hz,
* Higher Alpha: from 10 to 13 Hz,
* Beta: from 13 to 25 Hz,
* Gamma: from 25 to 40 Hz.
Parameters
----------
signal array
Filtered EEG signal matrix; each column is one EEG channel.
sampling_rate : int, float, optional
Sampling frequency (Hz).
size : float, optional
Window size (seconds).
overlap : float, optional
Window overlap (0 to 1).
Returns
-------
ts : array
Features time axis reference (seconds).
theta : array
Average power in the 4 to 8 Hz frequency band; each column is one EEG
channel.
alpha_low : array
Average power in the 8 to 10 Hz frequency band; each column is one EEG
channel.
alpha_high : array
Average power in the 10 to 13 Hz frequency band; each column is one EEG
channel.
beta : array
Average power in the 13 to 25 Hz frequency band; each column is one EEG
channel.
gamma : array
Average power in the 25 to 40 Hz frequency band; each column is one EEG
channel.
"""
# check inputs
if signal is None:
raise TypeError("Please specify an input signal.")
# ensure numpy
signal = np.array(signal)
nch = signal.shape[1]
sampling_rate = float(sampling_rate)
# convert sizes to samples
size = int(size * sampling_rate)
step = size - int(overlap * size)
# padding
min_pad = 1024
pad = None
if size < min_pad:
pad = min_pad - size
# frequency bands
bands = [[4, 8], [8, 10], [10, 13], [13, 25], [25, 40]]
nb = len(bands)
# windower
fcn_kwargs = {'sampling_rate': sampling_rate, 'bands': bands, 'pad': pad}
index, values = st.windower(signal=signal,
size=size,
step=step,
kernel='hann',
fcn=_power_features,
fcn_kwargs=fcn_kwargs)
# median filter
md_size = int(0.625 * sampling_rate / float(step))
if md_size % 2 == 0:
# must be odd
md_size += 1
for i in range(nb):
for j in range(nch):
values[:, i, j], _ = st.smoother(signal=values[:, i, j],
kernel='median',
size=md_size)
# extract individual bands
theta = values[:, 0, :]
alpha_low = values[:, 1, :]
alpha_high = values[:, 2, :]
beta = values[:, 3, :]
gamma = values[:, 4, :]
# convert indices to seconds
ts = index.astype('float') / sampling_rate
# output
args = (ts, theta, alpha_low, alpha_high, beta, gamma)
names = ('ts', 'theta', 'alpha_low', 'alpha_high', 'beta', 'gamma')
return utils.ReturnTuple(args, names)
def get_plf_features(signal=None, sampling_rate=1000., size=0.25, overlap=0.5):
"""Extract Phase-Locking Factor (PLF) features from EEG signals between all
channel pairs.
Parameters
----------
signal : array
Filtered EEG signal matrix; each column is one EEG channel.
sampling_rate : int, float, optional
Sampling frequency (Hz).
size : float, optional
Window size (seconds).
overlap : float, optional
Window overlap (0 to 1).
Returns
-------
ts : array
Features time axis reference (seconds).
plf_pairs : list
PLF pair indices.
plf : array
PLF matrix; each column is a channel pair.
"""
# check inputs
if signal is None:
raise TypeError("Please specify an input signal.")
# ensure numpy
signal = np.array(signal)
nch = signal.shape[1]
sampling_rate = float(sampling_rate)
# convert sizes to samples
size = int(size * sampling_rate)
step = size - int(overlap * size)
# padding
min_pad = 1024
N = None
if size < min_pad:
N = min_pad
# PLF pairs
pairs = [(i, j) for i in range(nch) for j in range(i + 1, nch)]
nb = len(pairs)
# windower
fcn_kwargs = {'pairs': pairs, 'N': N}
index, values = st.windower(signal=signal,
size=size,
step=step,
kernel='hann',
fcn=_plf_features,
fcn_kwargs=fcn_kwargs)
# median filter
md_size = int(0.625 * sampling_rate / float(step))
if md_size % 2 == 0:
# must be odd
md_size += 1
for i in range(nb):
values[:, i], _ = st.smoother(signal=values[:, i],
kernel='median',
size=md_size)
# convert indices to seconds
ts = index.astype('float') / sampling_rate
# output
args = (ts, pairs, values)
names = ('ts', 'plf_pairs', 'plf')
return utils.ReturnTuple(args, names)
def _power_features(signal=None, sampling_rate=1000., bands=None, pad=0):
"""Helper function to compute band power features for each window.
Parameters
----------
signal : array
Filtered EEG signal matrix; each column is one EEG channel.
sampling_rate : int, float, optional
Sampling frequency (Hz).
bands : list
List of frequency pairs defining the bands.
pad : int, optional
Padding for the Fourier Transform (number of zeros added).
Returns
-------
out : array
Average power for each band and EEG channel; shape is
(bands, channels).
"""
nch = signal.shape[1]
out = np.zeros((len(bands), nch), dtype='float')
for i in range(nch):
# compute power spectrum
freqs, power = st.power_spectrum(signal=signal[:, i],
sampling_rate=sampling_rate,
pad=pad,
pow2=False,
decibel=False)
# compute average band power
for j, b in enumerate(bands):
avg, = st.band_power(freqs=freqs,
power=power,
frequency=b,
decibel=False)
out[j, i] = avg
return out
def _plf_features(signal=None, pairs=None, N=None):
"""Helper function to compute PLF features for each window.
Parameters
----------
signal : array
Filtered EEG signal matrix; each column is one EEG channel.
pairs : iterable
List of signal channel pairs.
N : int, optional
Number of Fourier components.
Returns
-------
out : array
PLF for each channel pair.
"""
out = np.zeros(len(pairs), dtype='float')
for i, p in enumerate(pairs):
# compute PLF
s1 = signal[:, p[0]]
s2 = signal[:, p[1]]
out[i], = st.phase_locking(signal1=s1, signal2=s2, N=N)
return out