--- a
+++ b/features.py
@@ -0,0 +1,132 @@
+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