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
%gui qt from PyQt5 import QtCore from cognigraph.pipeline import Pipeline from cognigraph.nodes import sources, processors, outputs from cognigraph.gui.window import GUIWindow pipeline = Pipeline() file_path = r"C:\Users\evgenii\Downloads\brainvision\Bulavenkova_A_2017-10-24_15-33-18_Rest.vmrk" pipeline.source = sources.FileSource(file_path=file_path) # linear_filter = processors.LinearFilter(lower_cutoff=0.1, upper_cutoff=40) # pipeline.add_processor(linear_filter) # pipeline.add_processor(processors.MNE(method='MNE')) # pipeline.add_processor(processors.EnvelopeExtractor()) # pipeline.add_output(outputs.BrainViewer()) # pipeline.add_output(outputs.LSLStreamOutput()) # pipeline.initialize_all_nodes() window = GUIWindow(pipeline=pipeline) window.init_ui() window.setWindowFlags(QtCore.Qt.WindowStaysOnTopHint) window.show()
cur_dir = '/home/dmalt/Code/python/cogni_submodules' test_data_path = cur_dir + '/tests/data/' print(test_data_path) sim_data_fname = 'raw_sim_nobads.fif' # sim_data_fname = 'Koleno.fif' # fwd_fname = 'dmalt_custom_lr-fwd.fif' fwd_fname = 'dmalt_custom_mr-fwd.fif' # fwd_fname = 'sample_1005-eeg-oct-6-fwd.fif' surf_dir = '/home/dmalt/mne_data/MNE-sample-data/subjects/sample/surf' fwd_path = op.join(test_data_path, fwd_fname) sim_data_path = op.join(test_data_path, sim_data_fname) source = sources.FileSource(file_path=sim_data_path) source.loop_the_file = True source.MAX_SAMPLES_IN_CHUNK = 10000 pipeline.source = source # Processors preprocessing = processors.Preprocessing(collect_for_x_seconds=30) pipeline.add_processor(preprocessing) linear_filter = processors.LinearFilter(lower_cutoff=8.0, upper_cutoff=12.0) pipeline.add_processor(linear_filter) inverse_model = processors.MCE(forward_model_path=fwd_path, snr=1.0) # inverse_model = processors.InverseModel(method='MNE', forward_model_path=fwd_path, snr=1.0) pipeline.add_processor(inverse_model)
if not args.forward:
try:
fwd_tuple = QtGui.QFileDialog.getOpenFileName(
caption="Select forward model",
filter="MNE-python forward (*-fwd.fif)")
fwd_path = fwd_tuple[0]
except:
print("DATA FILE IS MANDATORY!")
else:
fwd_path = args.forward.name
if not fwd_path:
raise Exception("FORWARD SOLUTION IS MANDATORY!")
source = sources.FileSource(file_path=file_path)
# source = sources.FileSource()
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)
inverse_model = processors.InverseModel(method='MNE',
snr=1.0,
forward_model_path=fwd_path)
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