def assemble_pipeline(
file_path=None,
fwd_path=None,
subject=None,
subjects_dir=None,
inverse_method="mne",
):
pipeline = Pipeline()
source = sources.FileSource(file_path=file_path)
source.loop_the_file = True
source.MAX_SAMPLES_IN_CHUNK = 1000
pipeline.add_child(source)
# ----------------------------- processors ----------------------------- #
preprocessing = processors.Preprocessing(collect_for_x_seconds=10)
source.add_child(preprocessing)
linear_filter = processors.LinearFilter(lower_cutoff=8.0,
upper_cutoff=12.0)
preprocessing.add_child(linear_filter)
if inverse_method == "mne":
inverse_model = processors.MNE(method="MNE",
snr=1.0,
forward_model_path=fwd_path)
# inverse_model = processors.MneGcs(snr=1.0, seed=1000,
# forward_model_path=fwd_path)
linear_filter.add_child(inverse_model)
envelope_extractor = processors.EnvelopeExtractor(0.99)
inverse_model.add_child(envelope_extractor)
elif inverse_method == "beamformer":
inverse_model = processors.Beamformer(
fwd_path=fwd_path,
is_adaptive=True,
output_type="activation",
forgetting_factor_per_second=0.95,
)
linear_filter.add_child(inverse_model)
envelope_extractor = processors.EnvelopeExtractor(0.99)
inverse_model.add_child(envelope_extractor)
elif inverse_method == "mce":
inverse_model = processors.MCE(forward_model_path=fwd_path, snr=1.0)
linear_filter.add_child(inverse_model)
envelope_extractor = processors.EnvelopeExtractor(0.995)
inverse_model.add_child(envelope_extractor)
# ---------------------------------------------------------------------- #
# ------------------------------ outputs ------------------------------ #
global_mode = outputs.BrainViewer.LIMITS_MODES.GLOBAL
brain_viewer = outputs.BrainViewer(limits_mode=global_mode,
buffer_length=6)
envelope_extractor.add_child(brain_viewer)
return pipeline
data_path = op.join(test_data_path, data_fname) source = sources.FifSource(file_path=data_path) pipeline.source = source # Processors preprocessing = processors.Preprocessing(collect_for_x_seconds=0) pipeline.add_processor(preprocessing) ica_rejection = processors.ICARejection(collect_for_x_seconds=10) pipeline.add_processor(ica_rejection) linear_filter = processors.LinearFilter(lower_cutoff=8, upper_cutoff=12) pipeline.add_processor(linear_filter) inverse_model = processors.InverseModel(method='dSPM', snr=1.0) pipeline.add_processor(inverse_model) envelope_extractor = processors.EnvelopeExtractor(0.99) pipeline.add_processor(envelope_extractor) # Outputs signal_viewer = outputs.SignalViewer() pipeline.add_output(signal_viewer, parent=linear_filter) global_mode = outputs.BrainViewer.LIMITS_MODES.GLOBAL three_dee_brain = outputs.BrainViewer(limits_mode=global_mode,
def assemble_pipeline(file_path=None,
fwd_path=None,
subject=None,
subjects_dir=None,
inverse_method='mne'):
pipeline = Pipeline()
source = sources.FileSource(file_path=file_path)
source.loop_the_file = True
source.MAX_SAMPLES_IN_CHUNK = 10000
pipeline.source = source
# ----------------------------- processors ----------------------------- #
preprocessing = processors.Preprocessing(collect_for_x_seconds=120)
pipeline.add_processor(preprocessing)
linear_filter = processors.LinearFilter(lower_cutoff=8.0,
upper_cutoff=12.0)
pipeline.add_processor(linear_filter)
if inverse_method == 'mne':
inverse_model = processors.InverseModel(method='MNE',
snr=1.0,
forward_model_path=fwd_path)
# inverse_model = processors.MneGcs(snr=1.0, seed=1000,
# forward_model_path=fwd_path)
pipeline.add_processor(inverse_model)
envelope_extractor = processors.EnvelopeExtractor(0.99)
pipeline.add_processor(envelope_extractor)
elif inverse_method == 'beamformer':
inverse_model = processors.Beamformer(
forward_model_path=fwd_path,
is_adaptive=True,
output_type='activation',
forgetting_factor_per_second=0.95)
pipeline.add_processor(inverse_model)
envelope_extractor = processors.EnvelopeExtractor(0.99)
pipeline.add_processor(envelope_extractor)
elif inverse_method == 'mce':
inverse_model = processors.MCE(forward_model_path=fwd_path, snr=1.0)
pipeline.add_processor(inverse_model)
envelope_extractor = processors.EnvelopeExtractor(0.995)
pipeline.add_processor(envelope_extractor)
# ---------------------------------------------------------------------- #
# ------------------------------ outputs ------------------------------ #
global_mode = outputs.BrainViewer.LIMITS_MODES.GLOBAL
brain_viewer = outputs.BrainViewer(limits_mode=global_mode,
buffer_length=6,
surfaces_dir=None)
pipeline.add_output(brain_viewer, parent=envelope_extractor)
# roi_average = processors.AtlasViewer(SUBJECT, subjects_dir)
# roi_average.parent = inverse_model
# pipeline.add_processor(roi_average)
# aec = processors.AmplitudeEnvelopeCorrelations(
# method=None,
# seed=1000
# # method='temporal_orthogonalization',
# # method='geometric_correction',
# # seed=0
# )
# pipeline.add_processor(aec)
# aec.parent = inverse_model
# # coh = processors.Coherence(
# # method='coh', seed=0)
# aec_env = processors.EnvelopeExtractor(0.995)
# pipeline.add_processor(aec_env)
# seed_viewer = outputs.BrainViewer(
# limits_mode=global_mode, buffer_length=6,
# surfaces_dir=op.join(subjects_dir, SUBJECT))
# pipeline.add_output(seed_viewer, parent=aec_env)
# pipeline.add_output(outputs.LSLStreamOutput())
# signal_viewer = outputs.SignalViewer()
# signal_viewer_src = outputs.SignalViewer()
# pipeline.add_output(signal_viewer, parent=linear_filter)
# pipeline.add_output(signal_viewer_src, parent=roi_average)
# con_viewer = outputs.ConnectivityViewer(
# surfaces_dir=op.join(subjects_dir, SUBJECT))
# pipeline.add_output(con_viewer, parent=aec)
# --------------------------------------------------------------------- #
return pipeline