ON007763: meg dataset, 35 subjects#
BCCWJ-MEG
Access recordings and metadata through EEGDash.
Citation: Yushi Sugimoto, Masayuki Asahara, Hyeonjeong Jeong, Akitake Kanno, Masatoshi Koizumi, Yohei Oseki (2001). BCCWJ-MEG. 10.82901/nemar.on007763
Modality: meg Subjects: 35 Recordings: 35 License: CC0 Source: nemar
Metadata: Complete (100%)
35-participant MEG dataset — BCCWJ-MEG.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON007763
dataset = ON007763(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON007763(cache_dir="./data", subject="01")
Advanced query
dataset = ON007763(
cache_dir="./data",
query={"subject": {"$in": ["01", "02"]}},
)
Iterate recordings
for rec in dataset:
print(rec.subject, rec.raw.info['sfreq'])
If you use this dataset in your research, please cite the original authors.
BibTeX
@dataset{on007763,
title = {BCCWJ-MEG},
author = {Yushi Sugimoto and Masayuki Asahara and Hyeonjeong Jeong and Akitake Kanno and Masatoshi Koizumi and Yohei Oseki},
doi = {10.82901/nemar.on007763},
url = {https://doi.org/10.82901/nemar.on007763},
}
About This Dataset#
This dataset includes MEG recordings from Japanese native speakers who read Japanese newspaper articles word by word. This dataset is part of BCCWJ-Brain; three types of brain data (fMRI, MEG, and EEG) were acquired from separate groups of participants using the same stimuli, enabling cross-modality comparisons of language processing with high spatial and temporal resolution respectively.
The BCCWJ-Brain collection consists of the following datasets:
BCCWJ-fMRI: ds007752 (https://openneuro.org/datasets/ds007752)
BCCWJ-MEG: ds007763 (https://openneuro.org/datasets/ds007763)
BCCWJ-EEG: ds007753 (https://openneuro.org/datasets/ds007753)
Overview
Participants
Data from thirty-five participants were included in the dataset (13 females and 22 males; mean age= 21.7 (SD = 2.75)). All participants were right-handed, had no neurological illness, and had normal or corrected-to-normal vision.
Data Acquisition
View full README
Overview
Participants
Data from thirty-five participants were included in the dataset (13 females and 22 males; mean age= 21.7 (SD = 2.75)). All participants were right-handed, had no neurological illness, and had normal or corrected-to-normal vision.
Data Acquisition
Continuous MEG was recorded with a 200-channel whole-head MEG system with axial gradiometers (RIOCH Ltd., Tokyo, Japan) in a shielded room at a sampling rate of 1,000 Hz with an online low-pass filter of 200 Hz. T1-weighted images were also collected; slice thickness of 1 mm, field of view of 256 × 25699 mm, matrix size of 368 × 368, repetition time (TR) of 1,100 ms, and echo time (TE) of 5.1ms. Facial structures were removed from T1-weighted images using PyDeface (Gulban et al., 2022).
Experiment Procedure
Twenty Japanese newspaper articles were used as stimuli. Stimuli were presented word by word using rapid serial visual presentation (RSVP) implemented in PsychoPy (Peirce, 2007, 2009). Each stimulus was presented for 500 ms, followed by a 500 ms blank screen. The order of the newspaper articles were randomized.
Data Preprocessing
MEG data were preprocessed using MNE-Python (v1.9.0; Gramfort et al., 2013) and Eelbrain (v0.40.3; Brodbeck et al., 2023). We applied Continuously Adjusted least Square Method (CALM) filter (Adachi et al., 2001), then the continuous MEG data were combined with digitized files and converted into raw.fif files for further analysis. Independent component analysis (ICA) was then applied, and components reflecting ocular artifacts were identified and removed. The ICA decomposition was subsequently applied to the 0.1–40 Hz bandpass filtered data. Data were then segmented into epochs from −100 to 1,000 ms relative to word onset and downsampled to 200 Hz. Baseline correction was applied using the pre-stimulus interval (−100 to 0 ms). The preprocessed files are in derivatives.
derivatives/
├── meg/
├── sub-XX_task-BCCWJreading_meg_clm_raw.fif (raw data converted to fif files, applying CALM filter, without downsampling (1,000Hz))
├── sub-XX_task-BCCWJreading_meg_0.1-40-ica_raw.fif (preprocessed files)
└── sub-XX_task-BCCWJreading_meg_0.1-40-ica_ave.fif (evoked files)
Notes
Since the BCCWJ texts are not copyright-free, texts for the experiment is not included in this dataset. To obtain the text, users must register for access to BCCWJ (https://bccwj-data.ninjal.ac.jp/) separately. See https://clrd.ninjal.ac.jp/bccwj/en/subscription.html for the details. Once access is granted, we provide a script to incorporate the text into the corresponding events.tsv files.
References
Adachi, Y., Shimogawara, M., Higuchi, M., Haruta, Y., & Ochiai, M. (2001). Reduction of non-periodic environmental magnetic noise in MEG measurement by continuously adjusted least squares method. IEEE Transactions on Applied Superconductivity, 11(1), 669–672. https://doi.org/10.1109/77.919433 Brodbeck, C., Das, P., Gillis, M., Kulasingham, J. P., Bhattasali, S., Gaston, P., Resnik, P., & Simon, J. Z. (2023). Eelbrain, a Python toolkit for time-continuous analysis with temporal response functions. eLife, 12, e85012. https://doi.org/10.7554/eLife.85012 Gulban, O. F., Nielson, D., Poldrack, Lee, J., R., Gorgolewski, C., Vanessasaurus, & Ghosh, S. (2022). poldracklab/pydeface: v2.0.2 [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.3524401 Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., Goj, R., Jas, M., Brooks, T., Parkkonen, L., & Hämäläinen, M. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in Neuroinformatics, 7, 267. https://doi.org/10.3389/fnins.2013.00267 Maekawa, K., Yamazaki, M., Ogiso, T., Maruyama, T., Ogura, H., Kashino, W., Koiso, H., Yamaguchi, M., Tanaka, M., & Den, Y. (2014). Balanced corpus of contemporary written Japanese. Language Resources and Evaluation, 48, 345–371. https://doi.org/10.1007/s10579-013-9261-0 Peirce, J. W. (2007). PsychoPy—Psychophysics software in Python. Journal of Neuroscience Methods, 162(1–2), 8–13. https://doi.org/10.1016/j.jneumeth.2006.11.017 Peirce, J. W. (2009). Generating stimuli for neuroscience using PsychoPy. Frontiers in Neuroinformatics, 2, 10. https://doi.org/10.3389/neuro.11.010.2008
Cohort#
Dataset Statistics#
Age distribution by gender (n=35, range 18–31 yr, mean 21.7 yr)
Sex composition
Channel counts: 226 ch (n=35 recordings)
Sampling frequencies: 1000.0 Hz (n=35 recordings)
Total recording duration: 21 h 52 min
Signal · Electrodes & live trace#
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
BCCWJ-MEG |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2001 |
Authors |
Yushi Sugimoto, Masayuki Asahara, Hyeonjeong Jeong, Akitake Kanno, Masatoshi Koizumi, Yohei Oseki |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on007763,
title = {BCCWJ-MEG},
author = {Yushi Sugimoto and Masayuki Asahara and Hyeonjeong Jeong and Akitake Kanno and Masatoshi Koizumi and Yohei Oseki},
doi = {10.82901/nemar.on007763},
url = {https://doi.org/10.82901/nemar.on007763},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON007763(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
BCCWJ-MEG
- Study:
on007763(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON007763.Modality:
meg; Subject type:Unknown. Subjects: 35; recordings: 35; tasks: 1.- Parameters:
cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key
dataset.s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to
EEGDashDataset.
- data_dir#
Local dataset cache directory (
cache_dir / dataset_id).- Type:
Path
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/on007763 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on007763 DOI: https://doi.org/10.82901/nemar.on007763
Examples
>>> from eegdash.dataset import ON007763 >>> dataset = ON007763(cache_dir="./data") >>> recording = dataset[0] >>> raw = recording.load()
- __init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
- save(path: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for on007763 to reproduce the tutorial on this dataset.
Citation
Yushi Sugimoto, Masayuki Asahara, Hyeonjeong Jeong, Akitake Kanno, Masatoshi Koizumi, … (2001). BCCWJ-MEG. 10.82901/nemar.on007763
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on007763.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset