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features.py #12

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63 changes: 0 additions & 63 deletions gumpy/features.py
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Original file line number Diff line number Diff line change
Expand Up @@ -249,38 +249,7 @@ def PCA_dim_red(features, var_desired):
return features_reduced
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Hey, thanks for your very thoughtful comment! I agree with everything that you wrote. It hasn't reached the maturity I would love. However, the project is currently stale and we don't actively support it. If you open a reasonable pull request, I'm very inclined to have a look at it and potentially merge it :)



def RMS_features_extraction(data, trial_list, window_size, window_shift):
"""Extract RMS features from data

Args:
data: 2D (time points, Channels)
trial_list: list of the trials
window_size: Size of the window for extracting features
window_shift: size of the overalp

Returns:
The features matrix (trials, features)
"""
if window_shift > window_size:
raise ValueError("window_shift > window_size")

fs = data.sampling_freq

n_features = int(data.duration/(window_size-window_shift))

X = np.zeros((len(trial_list), n_features*4))

t = 0
for trial in trial_list:
# x3 is the worst of all with 43.3% average performance
x1=gumpy.signal.rms(trial[0], fs, window_size, window_shift)
x2=gumpy.signal.rms(trial[1], fs, window_size, window_shift)
x3=gumpy.signal.rms(trial[2], fs, window_size, window_shift)
x4=gumpy.signal.rms(trial[3], fs, window_size, window_shift)
x=np.concatenate((x1, x2, x3, x4))
X[t, :] = np.array([x])
t += 1
return X


def dwt_features(data, trials, level, sampling_freq, w, n, wavelet):
Expand Down Expand Up @@ -319,39 +288,7 @@ def dwt_features(data, trials, level, sampling_freq, w, n, wavelet):
return X


def alpha_subBP_features(data):
"""Extract alpha bands

Args:
data: 2D (time points, Channels)

Returns:
The alpha sub-bands
"""
# filter data in sub-bands by specification of low- and high-cut frequencies
alpha1 = gumpy.signal.butter_bandpass(data, 8.5, 11.5, order=6)
alpha2 = gumpy.signal.butter_bandpass(data, 9.0, 12.5, order=6)
alpha3 = gumpy.signal.butter_bandpass(data, 9.5, 11.5, order=6)
alpha4 = gumpy.signal.butter_bandpass(data, 8.0, 10.5, order=6)

# return a list of sub-bands
return [alpha1, alpha2, alpha3, alpha4]


def beta_subBP_features(data):
"""Extract beta bands

Args:
data: 2D (time points, Channels)

Returns:
The beta sub-bands
"""
beta1 = gumpy.signal.butter_bandpass(data, 14.0, 30.0, order=6)
beta2 = gumpy.signal.butter_bandpass(data, 16.0, 17.0, order=6)
beta3 = gumpy.signal.butter_bandpass(data, 17.0, 18.0, order=6)
beta4 = gumpy.signal.butter_bandpass(data, 18.0, 19.0, order=6)
return [beta1, beta2, beta3, beta4]


def powermean(data, trial, fs, w):
Expand Down