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,
buffer_length=6)
pipeline.add_output(three_dee_brain)
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)
pipeline.add_processor(inverse_model)
envelope_extractor = processors.EnvelopeExtractor(0.99)
pipeline.add_processor(envelope_extractor)
# Outputs
global_mode = outputs.ThreeDeeBrain.LIMITS_MODES.GLOBAL
three_dee_brain = outputs.ThreeDeeBrain(limits_mode=global_mode,
buffer_length=6,
surfaces_dir=SURF_DIR)
pipeline.add_output(three_dee_brain)
# pipeline.add_output(outputs.LSLStreamOutput())
signal_viewer = outputs.SignalViewer()
from cognigraph.pipeline import Pipeline from cognigraph.nodes import sources, processors, outputs from cognigraph import TIME_AXIS from cognigraph.gui.window import GUIWindow app = QtGui.QApplication(sys.argv) # BOBE pipeline = Pipeline() file_path = r"/home/evgenii/Downloads/brainvision/Bulavenkova_A_2017-10-24_15-33-18_Rest.vhdr" source = sources.FileSource(file_path=file_path) pipeline.source = source inverse = processors.InverseModel() pipeline.add_processor(inverse) three_dee = outputs.ThreeDeeBrain() pipeline.add_output(three_dee) pipeline.initialize_all_nodes() three_dee.widget.show() source.output = source.data.take(indices=(0,), axis=TIME_AXIS) inverse.update() three_dee.update() # Sample
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
pipeline = Pipeline()
source = sources.BrainvisionSource(file_path=launch_test_filepath)
source.loop_the_file = True
source.MAX_SAMPLES_IN_CHUNK = 30
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=3.0)
pipeline.add_processor(inverse_model)
envelope_extractor = processors.EnvelopeExtractor()
pipeline.add_processor(envelope_extractor)
# Outputs
global_mode = outputs.ThreeDeeBrain.LIMITS_MODES.GLOBAL
three_dee_brain = outputs.ThreeDeeBrain(limits_mode=global_mode,
buffer_length=6)
pipeline.add_output(three_dee_brain)
pipeline.add_output(outputs.LSLStreamOutput())
signal_viewer = outputs.SignalViewer()
pipeline.add_output(signal_viewer, input_node=linear_filter)