DS007870: meg dataset, 13 subjects#
Appleseed Audiobook MEG
Access recordings and metadata through EEGDash.
Citation: Brodbeck, Christian, Bhattasali, Shohini, Cruz Heredia, Aura AL, Resnik, Philip, Simon, Jonathan Z, Lau, Ellen (2026). Appleseed Audiobook MEG. 10.18112/openneuro.ds007870.v1.3.1
Modality: meg Subjects: 13 Recordings: 44 License: CC0 Source: openneuro
Metadata: Complete (100%)
13-participant MEG dataset — Appleseed Audiobook MEG.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS007870
dataset = DS007870(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS007870(cache_dir="./data", subject="01")
Advanced query
dataset = DS007870(
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{ds007870,
title = {Appleseed Audiobook MEG},
author = {Brodbeck, Christian and Bhattasali, Shohini and Cruz Heredia, Aura AL and Resnik, Philip and Simon, Jonathan Z and Lau, Ellen},
doi = {10.18112/openneuro.ds007870.v1.3.1},
url = {https://doi.org/10.18112/openneuro.ds007870.v1.3.1},
}
About This Dataset#
This dataset contains the Appleseed MEG data in Brain Imaging Data Structure (BIDS) standard formnat. It includes MEG recordings from 12 participants listening to continuous narrative speech, plus an empty-room recording, along with stimulus files and derivative outputs.
Please cite:
Appleseed MEG Dataset (BIDS)
Brodbeck, Christian, Shohini Bhattasali, Aura AL Cruz Heredia, Philip Resnik, Jonathan Z Simon, and Ellen Lau (2022). Parallel processing in speech perception with local and global representations of linguistic context. eLife, 11, e72056. https://doi.org/10.7554/elife.72056
Abstract
Speech processing is highly incremental. It is widely accepted that human listeners continuously use the linguistic context to anticipate upcoming concepts, words, and phonemes. However, previous evidence supports two seemingly contradictory models of how a predictive context is integrated with the bottom-up sensory input: Classic psycholinguistic paradigms suggest a two-stage process, in which acoustic input initially leads to local, context-independent representations, which are then quickly integrated with contextual constraints. This contrasts with the view that the brain constructs a single coherent, unified interpretation of the input, which fully integrates available information across representational hierarchies, and thus uses contextual constraints to modulate even the earliest sensory representations. To distinguish these hypotheses, we tested magnetoencephalography responses to continuous narrative speech for signatures of local and unified predictive models. Results provide evidence that listeners employ both types of models in parallel. Two local context models uniquely predict some part of early neural responses, one based on sublexical phoneme sequences, and one based on the phonemes in the current word alone; at the same time, even early responses to phonemes also reflect a unified model that incorporates sentence-level constraints to predict upcoming phonemes. Neural source localization places the anatomical origins of the different predictive models in nonidentical parts of the superior temporal lobes bilaterally, with the right hemisphere showing a relative preference for more local models. These results suggest that speech processing recruits both local and unified predictive models in parallel, reconciling previous disparate findings. Parallel models might make the perceptual system more robust, facilitate processing of unexpected inputs, and serve a function in language acquisition
Directory structure
View full README
Appleseed MEG Dataset (BIDS)
Brodbeck, Christian, Shohini Bhattasali, Aura AL Cruz Heredia, Philip Resnik, Jonathan Z Simon, and Ellen Lau (2022). Parallel processing in speech perception with local and global representations of linguistic context. eLife, 11, e72056. https://doi.org/10.7554/elife.72056
Abstract
Speech processing is highly incremental. It is widely accepted that human listeners continuously use the linguistic context to anticipate upcoming concepts, words, and phonemes. However, previous evidence supports two seemingly contradictory models of how a predictive context is integrated with the bottom-up sensory input: Classic psycholinguistic paradigms suggest a two-stage process, in which acoustic input initially leads to local, context-independent representations, which are then quickly integrated with contextual constraints. This contrasts with the view that the brain constructs a single coherent, unified interpretation of the input, which fully integrates available information across representational hierarchies, and thus uses contextual constraints to modulate even the earliest sensory representations. To distinguish these hypotheses, we tested magnetoencephalography responses to continuous narrative speech for signatures of local and unified predictive models. Results provide evidence that listeners employ both types of models in parallel. Two local context models uniquely predict some part of early neural responses, one based on sublexical phoneme sequences, and one based on the phonemes in the current word alone; at the same time, even early responses to phonemes also reflect a unified model that incorporates sentence-level constraints to predict upcoming phonemes. Neural source localization places the anatomical origins of the different predictive models in nonidentical parts of the superior temporal lobes bilaterally, with the right hemisphere showing a relative preference for more local models. These results suggest that speech processing recruits both local and unified predictive models in parallel, reconciling previous disparate findings. Parallel models might make the perceptual system more robust, facilitate processing of unexpected inputs, and serve a function in language acquisition
Directory structure
The repository currently contains the following top-level content:
BIDS/
├── README.md
├── dataset_description.json
├── participants.tsv
├── participants.json
├── stimuli/
│ ├── \*-gammatone.pickle
│ └── \*.TextGrid
├── sub-01/
│ └── meg/
├── sub-02/
│ └── meg/
├── ...
├── sub-12/
│ └── meg/
├── sub-emptyroom/
└── derivatives/
├── freesurfer/
├── mne/
├── predictors/
└── scripts/
Each subject folder contains a MEG data directory with BIDS-compliant files such as: - channel descriptions - event files - metadata JSON files - MEG FIF files - a scans.tsv file
Notes
Subjects sub-04 and sub-05 heard stimulus 11b instead of stimulus 11.
MRI data are not subject-specific anatomical scans; they are derived from the fsaverage template using subject-specific scaling parameters.
Original audio files are copyrighted and cannot be shared.
Data access and use
MEG data are stored in FIFF format and can be opened with MNE-Python. The BIDS metadata files describe the recording structure, events, channels, and task definitions for each subject.
Correspondance
Please contact Dr. Christian Brodbeck at brodbecc@mcmaster.ca for any further details or questions.
Cohort#
Dataset Statistics#
Channel counts: 193 ch (n=12 recordings)
Sampling frequencies: 1000.0 Hz (n=9 recordings)
Total recording duration: 18 min
Signal · Electrodes & live trace#
Live trace viewer — sub-02 · task-Appleseed
Showing one representative recording out of
13 subjects and 44 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _meg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?meg=<url>) to inspect it.
Electrode layout — MEG · 157 sensors — 157 channels
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 |
Appleseed Audiobook MEG |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2026 |
Authors |
Brodbeck, Christian, Bhattasali, Shohini, Cruz Heredia, Aura AL, Resnik, Philip, Simon, Jonathan Z, Lau, Ellen |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds007870,
title = {Appleseed Audiobook MEG},
author = {Brodbeck, Christian and Bhattasali, Shohini and Cruz Heredia, Aura AL and Resnik, Philip and Simon, Jonathan Z and Lau, Ellen},
doi = {10.18112/openneuro.ds007870.v1.3.1},
url = {https://doi.org/10.18112/openneuro.ds007870.v1.3.1},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.DS007870(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Appleseed Audiobook MEG
- Study:
ds007870(OpenNeuro)- Author (year):
—
- Canonical:
—
Also importable as:
DS007870.Modality:
meg; Subject type:Unknown. Subjects: 13; recordings: 44; tasks: 3.- 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/ds007870 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007870 DOI: https://doi.org/10.18112/openneuro.ds007870.v1.3.1
Examples
>>> from eegdash.dataset import DS007870 >>> dataset = DS007870(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 ds007870 to reproduce the tutorial on this dataset.
Citation
Brodbeck, Christian, Bhattasali, Shohini, Cruz Heredia, Aura AL, Resnik, Philip, Simon, Jonathan Z, … (2026). Appleseed Audiobook MEG. 10.18112/openneuro.ds007870.v1.3.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds007870.v1.3.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset