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Merged
deep-introspection merged 2 commits into from
Jun 21, 2023
Merged

compute_freq_bands adding filtering option #167

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f95c428
compute_freq_bands adding filtering option
micmar15sr May 19, 2023
864b9c8
Update analyses.py
deep-introspection Jun 21, 2023
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14 changes: 10 additions & 4 deletions hypyp/analyses.py
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Original file line number Diff line number Diff line change
Expand Up @@ -667,7 +667,7 @@ def compute_single_freq(data: np.ndarray, sampling_rate: int, freq_range: list)
return complex_signal


def compute_freq_bands(data: np.ndarray, sampling_rate: int, freq_bands: dict, **filter_options) -> np.ndarray:
def compute_freq_bands(data: np.ndarray, sampling_rate: int, freq_bands: dict, filter_signal: bool = True , **filter_options) -> np.ndarray:
"""
Computes analytic signal per frequency band using FIR filtering
and Hilbert transform.
Expand All @@ -678,9 +678,12 @@ def compute_freq_bands(data: np.ndarray, sampling_rate: int, freq_bands: dict, *
real-valued data to compute analytic signal from.
sampling_rate:
sampling rate.
filter_signal:
bool: whether to apply a filter on the signal,
default, true
freq_bands:
a dictionary specifying frequency band labels and corresponding frequency ranges
e.g. {'alpha':[8,12],'beta':[12,20]} indicates that computations are performed over two frequency bands: 8-12 Hz for the alpha band and 12-20 Hz for the beta band.
e.g. {'alpha':[8,12], 'beta':[12,20]} indicates that computations are performed over two frequency bands: 8-12 Hz for the alpha band and 12-20 Hz for the beta band.
**filter_options:
additional arguments for mne.filter.filter_data, such as filter_length, l_trans_bandwidth, h_trans_bandwidth
Returns:
Expand All @@ -690,16 +693,19 @@ def compute_freq_bands(data: np.ndarray, sampling_rate: int, freq_bands: dict, *
assert data[0].shape[0] == data[1].shape[0], "Two data streams should have the same number of trials."
data = np.array(data)

# filtering and hilbert transform
# filtering and Hilbert transform
complex_signal = []
for freq_band in freq_bands.values():
filtered = np.array([mne.filter.filter_data(data[participant],
if filter_signal:
filtered = np.array([mne.filter.filter_data(data[participant],
sampling_rate, l_freq=freq_band[0], h_freq=freq_band[1],
**filter_options,
verbose=False)
for participant in range(2)
# for each participant
])
else:
filtered=np.array([data[participant] for participant in range(2)])
hilb = signal.hilbert(filtered)
complex_signal.append(hilb)

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