import mne_features.univariate as mne_f
import numpy as np
def time_series_features(data):
'''
Computes the features variance, RMS and peak-to-peak amplitude using the package mne_features.
Args:
data (ndarray): EEG data.
Returns:
ndarray: Computed features.
'''
n_trials, n_secs, n_channels, _ = data.shape
features_per_channel = 3
features = np.empty([n_trials, n_secs, n_channels * features_per_channel])
for i, trial in enumerate(data):
for j, second in enumerate(trial):
variance = mne_f.compute_variance(second)
rms = mne_f.compute_rms(second)
ptp_amp = mne_f.compute_ptp_amp(second)
features[i][j] = np.concatenate([variance, rms, ptp_amp])
features = features.reshape(
[n_trials*n_secs, n_channels*features_per_channel])
return features
def freq_band_features(data, freq_bands):
'''
Computes the frequency bands delta, theta, alpha, beta and gamma using the package mne_features.
Args:
data (ndarray): EEG data.
freq_bands (ndarray): The frequency bands to compute.
Returns:
ndarray: Computed features.
'''
n_trials, n_secs, n_channels, sfreq = data.shape
features_per_channel = len(freq_bands)-1
features = np.empty([n_trials, n_secs, n_channels * features_per_channel])
for i, trial in enumerate(data):
for j, second in enumerate(trial):
psd = mne_f.compute_pow_freq_bands(
sfreq, second, freq_bands=freq_bands)
features[i][j] = psd
features = features.reshape(
[n_trials*n_secs, n_channels*features_per_channel])
return features
def hjorth_features(data):
'''
Computes the features Hjorth mobility (spectral) and Hjorth complexity (spectral) using the package mne_features.
Args:
data (ndarray): EEG data.
Returns:
ndarray: Computed features.
'''
n_trials, n_secs, n_channels, sfreq = data.shape
features_per_channel = 2
features = np.empty([n_trials, n_secs, n_channels * features_per_channel])
for i, trial in enumerate(data):
for j, second in enumerate(trial):
mobility_spect = mne_f.compute_hjorth_mobility_spect(sfreq, second)
complexity_spect = mne_f.compute_hjorth_complexity_spect(
sfreq, second)
features[i][j] = np.concatenate([mobility_spect, complexity_spect])
features = features.reshape(
[n_trials*n_secs, n_channels*features_per_channel])
return features
def fractal_features(data):
'''
Computes the Higuchi Fractal Dimension and Katz Fractal Dimension using the package mne_features.
Args:
data (ndarray): EEG data.
Returns:
ndarray: Computed features.
'''
n_trials, n_secs, n_channels, _ = data.shape
features_per_channel = 2
features = np.empty([n_trials, n_secs, n_channels * features_per_channel])
for i, trial in enumerate(data):
for j, second in enumerate(trial):
higuchi = mne_f.compute_higuchi_fd(second)
katz = mne_f.compute_katz_fd(second)
features[i][j] = np.concatenate([higuchi, katz])
features = features.reshape(
[n_trials*n_secs, n_channels*features_per_channel])
return features
def entropy_features(data):
'''
Computes the features Approximate Entropy, Sample Entropy, Spectral Entropy and SVD entropy using the package mne_features.
Args:
data (ndarray): EEG data.
Returns:
ndarray: Computed features.
'''
n_trials, n_secs, n_channels, sfreq = data.shape
features_per_channel = 4
features = np.empty([n_trials, n_secs, n_channels * features_per_channel])
for i, trial in enumerate(data):
for j, second in enumerate(trial):
app_entropy = mne_f.compute_app_entropy(second)
samp_entropy = mne_f.compute_samp_entropy(second)
spect_entropy = mne_f.compute_spect_entropy(sfreq, second)
svd_entropy = mne_f.compute_svd_entropy(second)
features[i][j] = np.concatenate(
[app_entropy, samp_entropy, spect_entropy, svd_entropy])
features = features.reshape(
[n_trials*n_secs, n_channels*features_per_channel])
return features
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