Finnish Letter-Sound Integration MEG Dataset

uploaded by Weiyong Xu on 2021-04-24 - 19 days ago
last modified on 2021-04-27 - 16 days ago
authored by Weiyong Xu, Orsolya Kolozsvari, Simo Monto, Robert Oostenveld, Jarmo Hämäläinen
0690791

OpenNeuro Accession Number: ds003634
Files: 289, Size: 35.99GB, Subjects: 29, Session: 1
Available Tasks: audiovisual
Available Modalities: T1w, meg, coordsystem, channels, events

README

========= Dataset description: Magnetoencephalography (MEG) dataset on Finnish letter-speech sound integration.

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 32 Finnish-speaking school children (6–11 years) participated in the study. Of those three were excluded for the following reasons: two subjects due to excessive head movements and one subject due to low head position in the MEG helmet.

EXPERIMENT

The stimuli consisted of eight Finnish capital letters (A, E, I, O, U, Y, Ä, and Ö) and their corresponding speech sounds ([a], [e], [i], [o], [u], [y], [æ], and [ø]). Four categories of stimuli, auditory-only (A), visual-only (V), audiovisual congruent (AVC), and audiovisual incongruent (AVI) were presented in random order with 112 trials for each type of stimuli. Trigger code: A:1 V:2 AVC:3 AVI:4

Cognitive tests

Cognitive tests results can be found in OSF: https://osf.io/6su5g/

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 MRIs were defaced using ft_defacevolume implemented in the fieldtrip toolbox.

REFERENCES

Xu, W., Kolozsvari, O. B., Monto, S. P., & Hämäläinen, J. A. (2018). Brain responses to letters and speech sounds and their correlations with cognitive skills related to reading in children. Frontiers in human neuroscience, 12, 304. https://doi.org/10.3389/fnhum.2018.00304

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. https://doi.org/10.1038/sdata.2018.110

Oostenveld, R., Fries, P., Maris, E., Schoffelen, JM (2011). FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Computational Intelligence and Neuroscience, Volume 2011 (2011), Article ID 156869, doi:10.1155/2011/156869

Authors

  • Weiyong Xu
  • Orsolya Kolozsvari
  • Simo Monto
  • Robert Oostenveld
  • Jarmo Hämäläinen

Dataset DOI

10.18112/openneuro.ds003634.v1.0.0

License

CC0

Acknowledgements

How to Acknowledge

Funding

  • ChildBrain (Marie Curie Innovative Training Networks, no. 641652)
  • Predictable (Marie Curie Innovative Training Networks, no. 641858)
  • Academy of Finland (MultiLeTe #292466)

Ethics Approvals

How To Cite

Copy
Weiyong Xu and Orsolya Kolozsvari and Simo Monto and Robert Oostenveld and Jarmo Hämäläinen (2021). Finnish Letter-Sound Integration MEG Dataset. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds003634.v1.0.0
More citation info

Finnish Letter-Sound Integration MEG Dataset

uploaded by Weiyong Xu on 2021-04-24 - 19 days ago
last modified on 2021-04-27 - 16 days ago
authored by Weiyong Xu, Orsolya Kolozsvari, Simo Monto, Robert Oostenveld, Jarmo Hämäläinen
0690791

OpenNeuro Accession Number: ds003634
Files: 289, Size: 35.99GB, Subjects: 29, Session: 1
Available Tasks: audiovisual
Available Modalities: T1w, meg, coordsystem, channels, events

README

========= Dataset description: Magnetoencephalography (MEG) dataset on Finnish letter-speech sound integration.

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 32 Finnish-speaking school children (6–11 years) participated in the study. Of those three were excluded for the following reasons: two subjects due to excessive head movements and one subject due to low head position in the MEG helmet.

EXPERIMENT

The stimuli consisted of eight Finnish capital letters (A, E, I, O, U, Y, Ä, and Ö) and their corresponding speech sounds ([a], [e], [i], [o], [u], [y], [æ], and [ø]). Four categories of stimuli, auditory-only (A), visual-only (V), audiovisual congruent (AVC), and audiovisual incongruent (AVI) were presented in random order with 112 trials for each type of stimuli. Trigger code: A:1 V:2 AVC:3 AVI:4

Cognitive tests

Cognitive tests results can be found in OSF: https://osf.io/6su5g/

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 MRIs were defaced using ft_defacevolume implemented in the fieldtrip toolbox.

REFERENCES

Xu, W., Kolozsvari, O. B., Monto, S. P., & Hämäläinen, J. A. (2018). Brain responses to letters and speech sounds and their correlations with cognitive skills related to reading in children. Frontiers in human neuroscience, 12, 304. https://doi.org/10.3389/fnhum.2018.00304

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. https://doi.org/10.1038/sdata.2018.110

Oostenveld, R., Fries, P., Maris, E., Schoffelen, JM (2011). FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Computational Intelligence and Neuroscience, Volume 2011 (2011), Article ID 156869, doi:10.1155/2011/156869

Authors

  • Weiyong Xu
  • Orsolya Kolozsvari
  • Simo Monto
  • Robert Oostenveld
  • Jarmo Hämäläinen

Dataset DOI

10.18112/openneuro.ds003634.v1.0.0

License

CC0

Acknowledgements

How to Acknowledge

Funding

  • ChildBrain (Marie Curie Innovative Training Networks, no. 641652)
  • Predictable (Marie Curie Innovative Training Networks, no. 641858)
  • Academy of Finland (MultiLeTe #292466)

Ethics Approvals

How To Cite

Copy
Weiyong Xu and Orsolya Kolozsvari and Simo Monto and Robert Oostenveld and Jarmo Hämäläinen (2021). Finnish Letter-Sound Integration MEG Dataset. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds003634.v1.0.0
More citation info

Dataset File Tree

Git Hash: 7b5981a 

BIDS Validation

Dataset File Tree

Git Hash: 7b5981a 

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