EEGdash›NeMAR›NM000471

Iss. 471 · 11 subjects · 22 recordings · CC0-1.0

Dataset Brief · Treder2021 hippocampal switchboard

NM000471: ieeg dataset, 11 subjects#

Treder2021 hippocampal switchboard: epoched intracranial EEG during object/scene localiser and associative recall (extra-hippocampal channels)

Access recordings and metadata through EEGDash.

Citation: Matthias S. Treder, Ian Charest, Sebastian Michelmann, María Carmen Martín-Buro, Frédéric Roux, Fernando Carceller-Benito, Arturo Ugalde-Canitrot, David T. Rollings, Vijay Sawlani, Ramesh Chelvarajah, Maria Wimber, Simon Hanslmayr, Bernhard P. Staresina (20). Treder2021 hippocampal switchboard: epoched intracranial EEG during object/scene localiser and associative recall (extra-hippocampal channels). 10.82901/nemar.nm000471

Modality: ieeg Subjects: 11 Recordings: 22 License: CC0-1.0 Source: nemar

Metadata: Complete (100%)

11-participant iEEG dataset — Treder2021 hippocampal switchboard: epoched intracranial EEG during object/scene localiser and associative recall (extra-hippocampal channels).

Data & curation Matthias S. Treder · Ian Charest · Sebastian Michelmann · María Carmen Martín-Buro · Frédéric Roux · Fernando Carceller-Benito · …
Senior author Bernhard P. Staresina
Year 20 · Distributed via NeMAR

iEEG · 41 (2), 27 (2), 101 (2), 34 (2), 112 (2), 109 (2), 82 (2), 48 (2), 93 (2), 91 (2), 115 (2) ch200 HzBIDS 1.10.02 tasks

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 NM000471

dataset = NM000471(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 = NM000471(cache_dir="./data", subject="01")

Advanced query

dataset = NM000471(
    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{nm000471,
  title = {Treder2021 hippocampal switchboard: epoched intracranial EEG during object/scene localiser and associative recall (extra-hippocampal channels)},
  author = {Matthias S. Treder and Ian Charest and Sebastian Michelmann and María Carmen Martín-Buro and Frédéric Roux and Fernando Carceller-Benito and Arturo Ugalde-Canitrot and David T. Rollings and Vijay Sawlani and Ramesh Chelvarajah and Maria Wimber and Simon Hanslmayr and Bernhard P. Staresina},
  doi = {10.82901/nemar.nm000471},
  url = {https://doi.org/10.82901/nemar.nm000471},
}

§ 02Study · The README

About This Dataset#

Epoched intracranial EEG from 11 patients (22 recordings, 9.03 h of epochs, 200 Hz) during an object/scene

localiser task and an associative recall task, released by the authors on OSF (https://osf.io/kpxw9/, CC0) with the MATLAB scripts that reproduce the PNAS figures.

FieldTrip epochs (single precision, 200 Hz), written back-to-back with one New Segment marker per trial; events.tsv has

each trial’s position in the file, the authors’ time axis start (-2 s) and every trialinfo field as released.

DOI

Treder et al. 2021, “The hippocampus as the switchboard between perception and memory”: intracranial EEG epochs

Channel names are the released anonymised labels channel01..N; no coordinates or anatomical labels are released for them (electrodes.tsv with n/a coordinates). Per the file names these are the extra-hippocampal channels; the hippocampal data of the release are time-frequency power only (*_recall_TFR_HIPP.mat, derived, not converted; listed in sourcedata/osf-kpxw9/NOT_COPIED_release_files.tsv together with the scalp-EEG experiment files of sharing/EEG/).
Signal units are not stated in the release (units = n/a; values unchanged). Preprocessing is described in the article.
sourcedata/osf-kpxw9/: the converted MAT files, the authors’ scripts and auxiliary files, unchanged.
Age and sex are not in the release.

Privacy

Channel names are anonymised by the authors; files hold trial numbers, responses and reaction times only.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000471-blue)](https://doi.org/10.82901/nemar.nm000471) # Treder et al. 2021, “The hippocampus as the switchboard between perception and memory”: intracranial EEG epochs Epoched intracranial EEG from 11 patients (22 recordings, 9.03 h of epochs, 200 Hz) during an object/scene localiser task and an associative recall task, released by the authors on OSF (https://osf.io/kpxw9/, CC0) with the MATLAB scripts that reproduce the PNAS figures. ## What is converted (DatasetType derivative) - sub-XX/ieeg/sub-XX_task-localiser_ieeg.vhdr and …_task-recall_ieeg.vhdr: the released s<NN>_<task>_EEG_extraHIPP.mat

FieldTrip epochs (single precision, 200 Hz), written back-to-back with one New Segment marker per trial; events.tsv has each trial’s position in the file, the authors’ time axis start (-2 s) and every trialinfo field as released.

Channel names are the released anonymised labels channel01..N; no coordinates or anatomical labels are released for them (electrodes.tsv with n/a coordinates). Per the file names these are the extra-hippocampal channels; the hippocampal data of the release are time-frequency power only (*_recall_TFR_HIPP.mat, derived, not converted; listed in sourcedata/osf-kpxw9/NOT_COPIED_release_files.tsv together with the scalp-EEG experiment files of sharing/EEG/).
Signal units are not stated in the release (units = n/a; values unchanged). Preprocessing is described in the article.
sourcedata/osf-kpxw9/: the converted MAT files, the authors’ scripts and auxiliary files, unchanged.
Age and sex are not in the release.

## Privacy Channel names are anonymised by the authors; files hold trial numbers, responses and reaction times only.

License: CC0-1.0

Authors:

Matthias S. Treder
Ian Charest
Sebastian Michelmann
María Carmen Martín-Buro
Frédéric Roux
… and 8 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000471

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

27344148829193101109112115

Sampling frequencies: 200.0 Hz (n=22 recordings)

Total recording duration: 9 h 1 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 41 (2), 27 (2), 101 (2), 34 (2), 112 (2), 109 (2), 82 (2), 48 (2), 93 (2), 91 (2), 115 (2) ch · iEEG · 200 Hz · 11 subjects, 22 recordings

Live trace viewer — sub-04 · task-localiser

Showing one representative recording out of 11 subjects and 22 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.

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.NM000471(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000471

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

class eegdash.dataset.NM000471(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Treder2021 hippocampal switchboard: epoched intracranial EEG during object/scene localiser and associative recall (extra-hippocampal channels)

Study:: nm000471 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000471.

Modality: ieeg; Subject type: Unknown. Subjects: 11; recordings: 22; tasks: 2.

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/nm000471 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000471 DOI: https://doi.org/10.82901/nemar.nm000471

Examples

>>> from eegdash.dataset import NM000471
>>> dataset = NM000471(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 — NM000471.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 nm000471 to reproduce the tutorial on this dataset.

Citation

Matthias S. Treder, Ian Charest, Sebastian Michelmann, María Carmen Martín-Buro, Frédéric Roux, … (20). Treder2021 hippocampal switchboard: epoched intracranial EEG during object/scene localiser and associative recall (extra-hippocampal channels). 10.82901/nemar.nm000471

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000471.

Related & sibling datasets

NM000400IEEG · 7 subj NM000462IEEG · 3 subj NM000409IEEG · 8 subj NM000476IEEG · 5 subj NM000437IEEG · 5 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0-1.0 · 10.82901/nemar.nm000471

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000471`
Title	Treder2021 hippocampal switchboard: epoched intracranial EEG during object/scene localiser and associative recall (extra-hippocampal channels)
Author (year)	—
Canonical	—
Importable as	`NM000471`
Year	20
Authors	Matthias S. Treder, Ian Charest, Sebastian Michelmann, María Carmen Martín-Buro, Frédéric Roux, Fernando Carceller-Benito, Arturo Ugalde-Canitrot, David T. Rollings, Vijay Sawlani, Ramesh Chelvarajah, Maria Wimber, Simon Hanslmayr, Bernhard P. Staresina
License	CC0-1.0
Citation / DOI	10.82901/nemar.nm000471
Source links	OpenNeuro \| NeMAR \| Source URL

NM000471: ieeg dataset, 11 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#