EEGdash›OpenNeuro›DS004837

Iss. 4837 · 60 subjects · 106 recordings · CC0

Dataset Brief · Magnetoencephalographic (MEG) Pitch and Duration Mismatch Neg…

DS004837: meg dataset, 60 subjects#

Magnetoencephalographic (MEG) Pitch and Duration Mismatch Negativity (MMN) in First-Episode Psychosis

Citation: Fran López-Caballero, Mark Curtis, Brian Coffman, Dean Salisbury (2023). Magnetoencephalographic (MEG) Pitch and Duration Mismatch Negativity (MMN) in First-Episode Psychosis. 10.18112/openneuro.ds004837.v1.0.2

60-participant MEG dataset — Magnetoencephalographic (MEG) Pitch and Duration Mismatch Negativity (MMN) in First-Episode Psychosis.

Data & curation Fran López-Caballero · Mark Curtis · Brian Coffman · Dean Salisbury
Senior author Dean Salisbury
Year 2023 · Distributed via OpenNeuro
Funding R01 MH108568 and MH113533 NIH/United States

3000 Hz · mixedBIDS 1.1.1Task · attnmodSchizophrenia/PsychosisAuditoryPerception

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 DS004837

dataset = DS004837(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 = DS004837(cache_dir="./data", subject="01")

Advanced query

dataset = DS004837(
    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{ds004837,
  title = {Magnetoencephalographic (MEG) Pitch and Duration Mismatch Negativity (MMN) in First-Episode Psychosis},
  author = {Fran López-Caballero and Mark Curtis and Brian Coffman and Dean Salisbury},
  doi = {10.18112/openneuro.ds004837.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004837.v1.0.2},
}

§ 02Study · The README

About This Dataset#

Project Title: Pittsburgh Early Psychosis Program (PEPP): Mismatch Negativity (MMN) in First-Episode Psychosis

Expected experimentation period:

Start date: 12/01/2017

End date: 09/15/2021 Project Description:

Oddball paradigm with standards, pitch deviants and duration deviants. Extended description at: https://doi.org/10.1111/ejn.16107 Participant categories:

Healthy controls (HC), First-episode Schizophrenia (FESZ), First-episode Affective Psychosis (FEAFF) Further information about clinical, neuropsychological, demographic and medication data can be found in derivatives/participants.csv Events: 1: standard 2: pitch deviant 3: duration deviant Funding:

National Institutes of Health (R01 MH108568 and MH113533)

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Sampling frequencies (Hz)

10003000

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage — ch · MEG · 3000 Hz · mixed · 60 subjects, 106 recordings

Live trace viewer — sub-2320A · ses-0001 · task-attnmod · run-01

Showing one representative recording out of 60 subjects and 106 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.

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

§ 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.DS004837(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)LopezCaballero2023

Canonical—

Importable asDS004837 · LopezCaballero2023

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

class eegdash.dataset.DS004837(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Magnetoencephalographic (MEG) Pitch and Duration Mismatch Negativity (MMN) in First-Episode Psychosis

Study:: ds004837 (OpenNeuro)
Author (year):: LopezCaballero2023
Canonical:: —

Also importable as: DS004837, LopezCaballero2023.

Modality: meg; Experiment type: Perception; Subject type: Schizophrenia/Psychosis. Subjects: 60; recordings: 106; 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/ds004837 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004837 DOI: https://doi.org/10.18112/openneuro.ds004837.v1.0.2 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004837
>>> dataset = DS004837(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 FacePre-bundled mirror at EEGDash/ds004837 · pull with datasets.load_dataset("EEGDash/ds004837").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004837.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 ds004837 to reproduce the tutorial on this dataset.

Citation

Fran López-Caballero, Mark Curtis, Brian Coffman, Dean Salisbury (2023). Magnetoencephalographic (MEG) Pitch and Duration Mismatch Negativity (MMN) in First-Episode Psychosis. 10.18112/openneuro.ds004837.v1.0.2

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds004837.v1.0.2.

Related & sibling datasets

DS000246MEG · 2 subj DS003703MEG · 34 subj DS006468MEG · 24 subj DS007523MEG · 58 subj DS004276MEG · 19 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.1.1

Sidecars

not yet probed

Provenance

CC0 · 10.18112/openneuro.ds004837.v1.0.2

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004837`
Title	Magnetoencephalographic (MEG) Pitch and Duration Mismatch Negativity (MMN) in First-Episode Psychosis
Author (year)	`LopezCaballero2023`
Canonical	—
Importable as	`DS004837`, `LopezCaballero2023`
Year	2023
Authors	Fran López-Caballero, Mark Curtis, Brian Coffman, Dean Salisbury
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004837.v1.0.2
Source links	OpenNeuro \| NeMAR \| Source URL

DS004837: meg dataset, 60 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#