MEG
Audiovisual Learning MEG Dataset

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BIDS Validation

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Audiovisual Learning MEG Dataset
Audiovisual Learning MEG Dataset
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README

========= Dataset description: Magnetoencephalography (MEG) dataset on grapheme-phoneme learning in 2 consecutive days

This MEG dataset was prepared in the Brain Imaging Data Structure (MEG-BIDS, Niso et al. 2018) format using MNE-BIDS (Appelhoff et al. 2019).

In total 36 people were scheduled to participate in the study. The data from 6 participants are not included in this dataset; 2 were excluded due to a low learning accuracy during the whole training sessions on Day 1, and for the other 4 participants MEG data were not measured because of cancellation.

EXPERIMENT

Day 1: 12 training and testing blocks Day 2: 6 training and testing blocks

The auditory and visual stimuli were 12 Georgian letters and 12 Finnish phonemes in 2 sets:

Set 1

a, ä, e, t, s, k

ჸ, ჵ, ჹ, უ, დ, ჱ

Set 2

o, ö, i, p, v, d

ც, ჴ, ნ, ფ, ღ, წ

Set 1 was used as Learnable (LB) and Set 2 was used as Control (CT) for participants with odd ID numbers. Set 2 was used as Learnable and Set 1 was used as Control for participants with even ID numbers.

The following triggers are included in the raw .fif files and are also in the “trigger” column of the events files:

LB/A/Training: 101,102,103,104, ...

LB/V/Training: 110,120,130,140, ...

LB/AVC/Training: 111,122,133,144, ...

LB/AVI/Training: 112,113,114,121,123,124,131,132,134,141,142,143, ...

LB/AVC/Testing: 311,322,333,344, ...

LB/AVI/Testing: 312,313,314,321,323,324,331,332,334,341,342,343, ...

CT/A/Training: 201,202,203,204, ...

CT/V/Training: 210,220,230,240, ...

CT/AV/Training: 212,213,214,221,223,224,231,232,234,241,242,243, ...

CT/AV/Testing: 412,413,414,421,423,424,431,432,434,441,442,443, ...

LB/YES: 511

LB/NO: 510

CT/UNKNOWN: 520

Test trial: 600

Correct button press: 610

Wrong button press: 630

MEG

Three anatomical landmarks were used to define the MEG head coordinate system: Nasion, LPA, and RPA.

The position of the HPI coils and the head shape (>100 points evenly distributed over the scalp) were digitized using the Polhemus Isotrak digital tracker system (Polhemus, Colchester, VT, United States).

MEG was recorded using the Elekta Neuromag TRIUX system (Elekta AB, Stockholm, Sweden) at the Centre for Interdisciplinary Brain Research, University of Jyväskylä.

Data were acquired from 306 MEG channels and 2 EOG channels with a sampling rate of 1000 Hz, an online band-pass filter of 0.1-330 Hz, and a 68° upright gantry position.

Maxfilter version 3.0.17 was used for movement compensation using temporal signal-space separation (tSSS).

Bad MEG channels were identified manually and were interpolated by Maxfilter.

MRI

Individual structural MRIs were not acquired. For source reconstruction, it is recommended to use a template, for example the “fsaverage“ brain from Freesurfer (https://surfer.nmr.mgh.harvard.edu/), and to scale the template head model and source space to the shape and size of the individual participants (as obtained from the head shape points).

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