{"dataset":{"id":"61270","dataset_id":"on007763","name":"BCCWJ-MEG","description":"This dataset comprises magnetoencephalography (MEG) recordings from 35 Japanese native speakers who read Japanese newspaper articles word by word, presented via rapid serial visual presentation. It forms part of the BCCWJ-Brain collection, which includes fMRI, MEG, and EEG data from separate participant groups exposed to the same stimuli, enabling cross-modal comparisons of language processing at high spatial and temporal resolution. T1-weighted structural images were also acquired for source localization purposes.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on007763","concept_doi":"10.82901/nemar.on007763","latest_version_doi":"10.82901/nemar.on007763.v1.0.0","created_at":"2026-07-06 02:31:15","updated_at":"2026-08-18 22:38:48","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"BCCWJ-MEG\",\n  \"description\": \"This dataset comprises magnetoencephalography (MEG) recordings from 35 Japanese native speakers who read Japanese newspaper articles word by word, presented via rapid serial visual presentation. It forms part of the BCCWJ-Brain collection, which includes fMRI, MEG, and EEG data from separate participant groups exposed to the same stimuli, enabling cross-modal comparisons of language processing at high spatial and temporal resolution. T1-weighted structural images were also acquired for source localization purposes.\",\n  \"methods_description\": \"Continuous MEG was recorded with a 200-channel whole-head MEG system using axial gradiometers at a sampling rate of 1,000 Hz with an online low-pass filter of 200 Hz. T1-weighted structural images were collected and defaced using PyDeface. Stimuli (twenty Japanese newspaper articles) were presented word by word via RSVP in PsychoPy, each word shown for 500 ms followed by a 500 ms blank screen. Preprocessing was performed using MNE-Python and Eelbrain, including CALM filtering, ICA-based artifact removal, 0.1–40 Hz bandpass filtering, epoching (-100 to 1000 ms relative to word onset), downsampling to 200 Hz, and baseline correction.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Yushi Sugimoto\": {},\n    \"Masayuki Asahara\": {},\n    \"Hyeonjeong Jeong\": {},\n    \"Akitake Kanno\": {},\n    \"Masatoshi Koizumi\": {},\n    \"Yohei Oseki\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"Magnetoencephalography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D015225\"\n    },\n    {\n      \"term\": \"Japanese language processing\"\n    },\n    {\n      \"term\": \"reading\"\n    },\n    {\n      \"term\": \"language comprehension\"\n    },\n    {\n      \"term\": \"MEG\"\n    },\n    {\n      \"term\": \"psycholinguistics\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on007763\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/on007763\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1109/77.919433\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.7554/eLife.85012\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.5281/zenodo.3524401\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.3389/fnins.2013.00267\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1007/s10579-013-9261-0\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1016/j.jneumeth.2006.11.017\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.3389/neuro.11.010.2008\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds007763.v1.1.1\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"resource_type_specific\": \"Structural MRI Dataset\",\n  \"modalities\": [\n    \"anat\",\n    \"meg\"\n  ],\n  \"sizes\": [\n    \"177.7 GB (287 files)\"\n  ],\n  \"formats\": [\n    \".con\",\n    \".fif\",\n    \".gz\",\n    \".json\",\n    \".md\",\n    \".mrk\",\n    \".pos\",\n    \".tsv\",\n    \".yml\"\n  ],\n  \"source_hash\": \"c6db924742cc846293727665851b6727b6f271a6c96ccd51a42dc0958a31ec90\"\n}","last_activity_at":"2026-07-06 02:31:15","source":"openneuro","source_id":"ds007763","subject_count":35,"modalities":"anat,meg","age_min":18,"age_max":31,"file_size":177737930062,"total_files":470,"tasks":"BCCWJreading","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Yushi Sugimoto, Masayuki Asahara, Hyeonjeong Jeong, Akitake Kanno, Masatoshi Koizumi, Yohei Oseki","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on007763-blue)](https://doi.org/10.82901/nemar.on007763)\n\n\n## Overview\nThis 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.\nThe BCCWJ-Brain collection consists of the following datasets:\n\n- BCCWJ-fMRI: ds007752 ([https://openneuro.org/datasets/ds007752](https://openneuro.org/datasets/ds007752))\n- BCCWJ-MEG: ds007763 ([https://openneuro.org/datasets/ds007763](https://openneuro.org/datasets/ds007763))\n- BCCWJ-EEG: ds007753 ([https://openneuro.org/datasets/ds007753](https://openneuro.org/datasets/ds007753))\n\n\n## Participants\nData 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. \n\n## Data Acquisition\nContinuous 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).\n\n## Experiment Procedure\nTwenty 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.\n\n## Data Preprocessing\nMEG 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`.\n\n```\nderivatives/\n├── meg/\n    ├── sub-XX_task-BCCWJreading_meg_clm_raw.fif (raw data converted to fif files, applying CALM filter, without downsampling (1,000Hz))\n    ├── sub-XX_task-BCCWJreading_meg_0.1-40-ica_raw.fif (preprocessed files)\n    └── sub-XX_task-BCCWJreading_meg_0.1-40-ica_ave.fif (evoked files)\n```\n\n## Notes\nSince 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/](https://bccwj-data.ninjal.ac.jp/)) separately.  See [https://clrd.ninjal.ac.jp/bccwj/en/subscription.html](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.\n\n\n## References\nAdachi, 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\n\nBrodbeck, 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\n\nGulban, 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\n\nGramfort, 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\n\nMaekawa, 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\n\nPeirce, 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\n\nPeirce, J. W. (2009). Generating stimuli for neuroscience using PsychoPy. Frontiers in Neuroinformatics, 2, 10. https://doi.org/10.3389/neuro.11.010.2008","bids_version":"1.9.0","sessions_count":null,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"failed","zarr_converted_at":"2026-07-10 03:10:27","zarr_store_count":105,"zarr_index_etag":"123670f69456d22bc0ca70d23cc16a53","zarr_source_commit":"a0945680252c57e1527150c29190811b4cf5bef9","archive_status":null,"archive_size":null,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":"dataset 165.5 GB exceeds 100.0 GB archive limit; use direct download","zarr_errors":35,"zarr_failure_count":35,"zarr_deterministic":0,"zarr_failed_at":"2026-08-27 20:58:34","num_dataset_citations":1,"num_datapaper_citations":73,"n_channels":null,"electrode_system":null,"has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":177735990537,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":0,"total_recording_duration":null,"recording_duration_min":null,"recording_duration_max":null,"recording_count":null,"recordings_unavailable":null,"recordings_measured":null,"channel_count_min":null,"channel_count_max":null,"sampling_frequency":null,"power_line_frequency":null,"eeg_reference":null,"placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-18 22:38:33\",\"metadata_updated_at\":\"2026-08-18 22:38:46\",\"archive_checked_at\":\"2026-07-06 02:45:46\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-07-06 02:46:16\",\"citations_updated_at\":\"2026-09-01 03:00:30\",\"channel_montage_checked_at\":null,\"hed_checked_at\":null,\"data_checked_at\":null,\"availability_report_at\":\"2026-07-23 01:33:44\",\"recording_stats_at\":null,\"signal_defaults_at\":\"2026-09-02 12:58:21\"}","participants":35,"num_citations":74,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"166 GB","zarr_data_failures":{"count":35,"detail_ref":"zarr/index.json","compacted_by":"migration_0074"},"zarr_index_url":null,"attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}