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robertoostenveldrobertoostenveld
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Merge pull request #24 from robertoostenveld/mne_sample_data
include example BIDS dataset from MNE
2 parents 43094b1 + b4a4bde commit 3d50bad

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‎mne_sample_data/acq-epi_T1w.json‎

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{
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"RepetitionTime": 2,
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"EchoTime": 0.095,
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"FlipAngle": 90,
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"Manufacturer": "Siemens",
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"ManufacturersModelName": "TIM TRIO",
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"MagneticFieldStrength": 3,
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"PulseSequenceType": "EPI"
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}

‎mne_sample_data/acq-flipangle05_run-01_MEFLASH.json‎

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{
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"RepetitionTime": 0.020,
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"FlipAngle": 5,
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"EchoTime": 0.00185
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}

‎mne_sample_data/acq-flipangle30_run-01_MEFLASH.json‎

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{
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"RepetitionTime": 0.020,
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"FlipAngle": 30,
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"EchoTime": 0.00185
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}

‎mne_sample_data/dataset_description.json‎

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{
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"BIDSVersion": "1.0.0rc4",
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"License": "This dataset is made available under the Creative Commons Attribution 4.0 International Public License, whose full text can be found at https://www.creativecommons.org/licenses/by/4.0/legalcode",
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"Name": "Audio visual paradigm known as the MNE software sample dataset"
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}

‎mne_sample_data/sub-01/anat/sub-01_acq-epi_T1w.nii.gz‎

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‎mne_sample_data/sub-01/anat/sub-01_acq-flipangle05_run-01_MEFLASH.nii.gz‎

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‎mne_sample_data/sub-01/anat/sub-01_acq-flipangle30_run-01_MEFLASH.nii.gz‎

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‎mne_sample_data/sub-01/meg/audvis.ave‎

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#
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# These are comments
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#
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average {
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#
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# Output files
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#
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# outfile sample_audvis-ave.fif
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# logfile sample_audvis-ave.log
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# eventfile sample_audvis.eve
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#
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# Rejection values
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#
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gradReject 2000e-13
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magReject 4e-12
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eegReject 0e-6
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eogReject 150e-6
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#
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# Category specifications
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#
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category {
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name "Left Auditory"
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event 1
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tmin -0.2
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tmax 0.5
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color 1 1 0
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}
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category {
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name "Right Auditory"
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event 2
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tmin -0.2
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tmax 0.5
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color 1 0 0
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}
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category {
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name "Left visual"
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event 3
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tmin -0.2
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tmax 0.5
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color 0 1 0
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}
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category {
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name "Right visual"
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event 4
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tmin -0.2
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tmax 0.5
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color 0.5 0.8 1
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}
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}

‎mne_sample_data/sub-01/meg/ct_sparse_mgh.fif‎

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‎mne_sample_data/sub-01/meg/eve_to_tsv.py‎

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import pandas as pd
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import mne
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events_fname = "/Users/alex/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw-eve.fif"
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raw_fname = "sub-01_task-audiovisual_run-01_meg.fif"
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events = mne.read_events(events_fname).astype(int)
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raw = mne.io.read_raw_fif(raw_fname)
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event_id = {'Auditory/Left': 1, 'Auditory/Right': 2, 'Visual/Left': 3,
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'Visual/Right': 4, 'Smiley': 5, 'Button': 32}
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events[:, 0] -= raw.first_samp
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event_id_map = {v: k for k, v in event_id.items()}
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df = pd.DataFrame(events[:, [0, 2]], columns=['Sample', 'Condition'])
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df.Condition = df.Condition.map(event_id_map)
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print(df.head())
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df.to_csv('sub-01_task-audiovisual_run-01_events.tsv', sep='\t', index=False)

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