2.4.3. Example of a events.tsv file generation: the neurospin/localizer events.

The protocol described is the so-called “archi standard” localizer event sequence. See Pinel et al., BMC neuroscience 2007 for reference.

print(__doc__)

Define the onset times in seconds. Those are typically extracted from the stimulation software used.

import numpy as np
onset = np.array([
    0., 2.4, 8.7, 11.4, 15., 18., 20.7, 23.7, 26.7, 29.7, 33., 35.4, 39.,
    41.7, 44.7, 48., 56.4, 59.7, 62.4, 69., 71.4, 75., 83.4, 87., 89.7,
    96., 108., 116.7, 119.4, 122.7, 125.4, 131.4, 135., 137.7, 140.4,
    143.4, 146.7, 149.4, 153., 156., 159., 162., 164.4, 167.7, 170.4,
    173.7, 176.7, 188.4, 191.7, 195., 198., 201., 203.7, 207., 210.,
    212.7, 215.7, 218.7, 221.4, 224.7, 227.7, 230.7, 234., 236.7, 246.,
    248.4, 251.7, 254.7, 257.4, 260.4, 264., 266.7, 269.7, 275.4, 278.4,
    284.4, 288., 291., 293.4, 296.7])

Associated trial types: these are numbered between 0 and 9, hence correspond to 10 different conditions.

trial_idx = np.array(
    [7, 7, 0, 2, 9, 4, 9, 3, 5, 9, 1, 6, 8, 8, 6, 6, 8, 0, 3, 4, 5, 8, 6,
     2, 9, 1, 6, 5, 9, 1, 7, 8, 6, 6, 1, 2, 9, 0, 7, 1, 8, 2, 7, 8, 3, 6,
     0, 0, 6, 8, 7, 7, 1, 1, 1, 5, 5, 0, 7, 0, 4, 2, 7, 9, 8, 0, 6, 3, 3,
     7, 1, 0, 0, 4, 1, 9, 8, 4, 9, 9])

We may want to map these indices to explicit condition names. For that, we define a list of 10 strings.

condition_ids = ['horizontal checkerboard',
                 'vertical checkerboard',
                 'right button press, auditory instructions',
                 'left button press, auditory instructions',
                 'right button press, visual instructions',
                 'left button press, visual instructions',
                 'mental computation, auditory instructions',
                 'mental computation, visual instructions',
                 'visual sentence',
                 'auditory sentence']

trial_type = np.array([condition_ids[i] for i in trial_idx])

We also define a duration (required by BIDS conventions).

duration = np.ones_like(onset)

Form an event dataframe from these information.

import pandas as pd
events = pd.DataFrame({'trial_type': trial_type,
                       'onset': onset,
                       'duration': duration})

Export them to a tsv file.

tsvfile = 'localizer_events.tsv'
events.to_csv(tsvfile, sep='\t', index=False)
print("Created the events file in %s " % tsvfile)