EEGdash›NeMAR›NM000473

Iss. 473 · 10 subjects · 10 recordings · CC-BY-4.0

Dataset Brief · Qin2023 action observation ECoG

NM000473: ieeg dataset, 10 subjects#

Qin2023 action observation ECoG: re-referenced region-of-interest electrocorticography during observation of predictable and unpredictable action movies

Access recordings and metadata through EEGDash.

Citation: Chaoyi Qin, Frederic Michon, Yoshiyuki Onuki, Yohei Ishishita, Keisuke Otani, Kensuke Kawai, Pascal Fries, Valeria Gazzola, Christian Keysers (20). Qin2023 action observation ECoG: re-referenced region-of-interest electrocorticography during observation of predictable and unpredictable action movies. 10.82901/nemar.nm000473

Modality: ieeg Subjects: 10 Recordings: 10 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

10-participant iEEG dataset — Qin2023 action observation ECoG: re-referenced region-of-interest electrocorticography during observation of predictable and unpredictable action movies.

Data & curation Chaoyi Qin · Frederic Michon · Yoshiyuki Onuki · Yohei Ishishita · Keisuke Otani · Kensuke Kawai · …
Senior author Christian Keysers
Year 20 · Distributed via NeMAR
Funding Japan Society for the Promotion of Science JP17H04305 · Japan Society for the Promotion of Science JP20H03794 · + 2 more

iEEG · 1, 9, 16, 18, 22, 17, 37, 14, 13, 8 ch1000 HzBIDS 1.10.0Task · actionobservation

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000473

dataset = NM000473(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = NM000473(cache_dir="./data", subject="01")

Advanced query

dataset = NM000473(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000473,
  title = {Qin2023 action observation ECoG: re-referenced region-of-interest electrocorticography during observation of predictable and unpredictable action movies},
  author = {Chaoyi Qin and Frederic Michon and Yoshiyuki Onuki and Yohei Ishishita and Keisuke Otani and Kensuke Kawai and Pascal Fries and Valeria Gazzola and Christian Keysers},
  doi = {10.82901/nemar.nm000473},
  url = {https://doi.org/10.82901/nemar.nm000473},
}

§ 02Study · The README

About This Dataset#

Continuous electrocorticography (10 patients, 8.70 h, 1000 Hz) recorded while epilepsy patients watched movies of

sequential actions, released by the authors on OSF (doi:10.17605/OSF.IO/4XJG7, CC-BY-4.0) as FieldTrip structures of the re-referenced region-of-interest channels used in the article, plus the event tables and stimuli.

hdr.chanunit, values unchanged, float32), channels = the released ROI contacts (names as released, e.g. A26, B55). The signals

are re-referenced by the authors (scheme in the article; not stated in the release) and restricted to ROI channels, hence derivative.

DOI

Qin et al. 2023, “Predictability alters information flow during action observation in human ECoG”: ROI channels

events.tsv: the released camInfo table of Patient<N>_eventdata.mat (camera-change triggers in the movies): sample, movie id, movie number, the S/M flag and the intervals before/after. Column meanings were inferred from the values and checked against the BORIS annotation files (see events.json and code/conversion_report.json).
electrodes.tsv: ACPC positions (elec.chanpos, FieldTrip coordsys ‘acpc’) and MNI positions (elec.elecposMNI, template not stated) for the ROI channels. Positions of all implanted contacts are in the sourcedata MAT files.
stimuli/: the three movie runs (avi), the BORIS camera-change annotations per movie (csv), the stimulus lists and the BORIS project file, as released. sourcedata/osf-4xjg7/: all release files unchanged.
Age and sex are not in the release. The patient numbers of the release (1,2,3,4,6,8,9,11,12,13) are kept as subject numbers.

Privacy

The release holds patient numbers only. FieldTrip cfg fields in the MAT files contain the authors’ analysis paths/usernames and analysis timestamps (2022-2023), not patient data.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000473-blue)](https://doi.org/10.82901/nemar.nm000473) # Qin et al. 2023, “Predictability alters information flow during action observation in human ECoG”: ROI channels Continuous electrocorticography (10 patients, 8.70 h, 1000 Hz) recorded while epilepsy patients watched movies of sequential actions, released by the authors on OSF (doi:10.17605/OSF.IO/4XJG7, CC-BY-4.0) as FieldTrip structures of the re-referenced region-of-interest channels used in the article, plus the event tables and stimuli. ## What is converted (DatasetType derivative) - sub-XX/ieeg/sub-XX_task-actionobservation_ieeg.vhdr: rerefData.trial{1} of Patient<N>LARER_rerefDataInROI.mat (µV per

hdr.chanunit, values unchanged, float32), channels = the released ROI contacts (names as released, e.g. A26, B55). The signals are re-referenced by the authors (scheme in the article; not stated in the release) and restricted to ROI channels, hence derivative.

events.tsv: the released camInfo table of Patient<N>_eventdata.mat (camera-change triggers in the movies): sample, movie id, movie number, the S/M flag and the intervals before/after. Column meanings were inferred from the values and checked against the BORIS annotation files (see events.json and code/conversion_report.json).
electrodes.tsv: ACPC positions (elec.chanpos, FieldTrip coordsys ‘acpc’) and MNI positions (elec.elecposMNI, template not stated) for the ROI channels. Positions of all implanted contacts are in the sourcedata MAT files.
stimuli/: the three movie runs (avi), the BORIS camera-change annotations per movie (csv), the stimulus lists and the BORIS project file, as released. sourcedata/osf-4xjg7/: all release files unchanged.
Age and sex are not in the release. The patient numbers of the release (1,2,3,4,6,8,9,11,12,13) are kept as subject numbers.

## Privacy The release holds patient numbers only. FieldTrip cfg fields in the MAT files contain the authors’ analysis paths/usernames and analysis timestamps (2022-2023), not patient data.

License: CC-BY-4.0

Authors:

Chaoyi Qin
Frederic Michon
Yoshiyuki Onuki
Yohei Ishishita
Keisuke Otani
… and 4 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000473

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

18913141617182237

Sampling frequencies: 1000.0 Hz (n=10 recordings)

Total recording duration: 8 h 41 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 1, 9, 16, 18, 22, 17, 37, 14, 13, 8 ch · iEEG · 1000 Hz · 10 subjects, 10 recordings

Live trace viewer — sub-04 · task-actionobservation

Showing one representative recording out of 10 subjects and 10 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _ieeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?ieeg=<url>) to inspect it.

Electrode layout — iEEG · 16 sensors — 16 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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000473(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000473

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000473(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Qin2023 action observation ECoG: re-referenced region-of-interest electrocorticography during observation of predictable and unpredictable action movies

Study:: nm000473 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000473.

Modality: ieeg; Subject type: Unknown. Subjects: 10; recordings: 10; 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

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000473 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000473 DOI: https://doi.org/10.82901/nemar.nm000473

Examples

>>> from eegdash.dataset import NM000473
>>> dataset = NM000473(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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000473.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 nm000473 to reproduce the tutorial on this dataset.

Citation

Chaoyi Qin, Frederic Michon, Yoshiyuki Onuki, Yohei Ishishita, Keisuke Otani, … (20). Qin2023 action observation ECoG: re-referenced region-of-interest electrocorticography during observation of predictable and unpredictable action movies. 10.82901/nemar.nm000473

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000473.

Related & sibling datasets

NM000482IEEG · 7 subj NM000480IEEG · 11 subj NM000481IEEG · 14 subj NM000405IEEG · 16 subj NM000460IEEG · 1 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000473

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000473`
Title	Qin2023 action observation ECoG: re-referenced region-of-interest electrocorticography during observation of predictable and unpredictable action movies
Author (year)	—
Canonical	—
Importable as	`NM000473`
Year	20
Authors	Chaoyi Qin, Frederic Michon, Yoshiyuki Onuki, Yohei Ishishita, Keisuke Otani, Kensuke Kawai, Pascal Fries, Valeria Gazzola, Christian Keysers
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000473
Source links	OpenNeuro \| NeMAR \| Source URL

NM000473: ieeg dataset, 10 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#