def compute_power_frequency_on_spectrum(signal, low_bound_freq, high_bound_freq=None):
"""Compute the total power frequency on a particular spectrum
Args:
signal: the input data (1d-array).
lower_bound_freq: the lower bound frequency below which the signal must be completely attenuated.
higher_bound_freq: the higher bound frequency above which the signal must be completely attenuated.
Returns:
total_power_frequency: a float that represents the total power frequency of the freq_spectrum.
"""
if high_bound_freq is None:
filtered_freq = high_pass_filter(signal, low_bound_freq, defaults.SAMPLING_FREQUENCY)
else:
filtered_freq = band_pass_filter(signal, low_bound_freq, high_bound_freq, defaults.SAMPLING_FREQUENCY)
return compute_total_power_frequency_on_signal(filtered_freq)
def signals_to_epochs(signal_dictionary):
"""Filter the signal data using BP and Notch filters.
Aggregate the signal data into epochs (30 seconds per epoch, 30 * sampling_frequency data per epoch)
Args:
signal_dictionary: dictionary of lists of EEG and EMG data
log: option to log the progress of the function
Returns:
epochs: dictionary of data_file dictionaries, each containing dictionaries of lists of filtered EEG and EMG data
"""
epochs = []
for channel, signal in signal_dictionary.iteritems():
# determine signal type and use the appropriate boundary frequencies for BP filter
if channel in (EMG1, EMG2):
low_bound_freq = EMG_LOW_BOUND_FREQUENCY
high_bound_freq = EMG_HIGH_BOUND_FREQUENCY
else:
low_bound_freq = EEG_LOW_BOUND_FREQUENCY
high_bound_freq = EEG_HIGH_BOUND_FREQUENCY
# apply BP filter
filtered_signal, _ = band_pass_filter(signal, low_bound_freq,
high_bound_freq, SAMPLING_FREQUENCY)
# apply Notch filter to remove AC interference
filtered_signal = notch_filter(filtered_signal, NOTCH_FREQUENCY,
NOTCH_FREQUENCY_BANDWIDTH, SAMPLING_FREQUENCY)
# aggregate data into epochs
start_idx = 0
end_idx = SAMPLES_PER_EPOCH
epoch_idx = 0
while end_idx <= len(filtered_signal):
if not epoch_idx < len(epochs):
epochs.append({})
epochs[epoch_idx][channel] = filtered_signal[start_idx:end_idx]
epoch_idx += 1
start_idx = end_idx
end_idx += SAMPLES_PER_EPOCH
return epochs
