EEGdash›OpenNeuro›DS004993

Iss. 4993 · 3 subjects · 3 recordings · CC0

Dataset Brief · WIRED ICM Sample Dataset - Workshop on Intracranial Recording…

DS004993: ieeg dataset, 3 subjects#

WIRED ICM Sample Dataset - Workshop on Intracranial Recordings in Humans, Epilepsy, DBS

Citation: Liberty S. Hamilton, Maansi Desai, Alyssa Field (2019). WIRED ICM Sample Dataset - Workshop on Intracranial Recordings in Humans, Epilepsy, DBS. 10.18112/openneuro.ds004993.v1.1.2

3-participant iEEG dataset — WIRED ICM Sample Dataset - Workshop on Intracranial Recordings in Humans, Epilepsy, DBS.

Data & curation Liberty S. Hamilton · Maansi Desai · Alyssa Field
Year 2019 · Distributed via OpenNeuro
Funding National Institutes of Health - National Institute on Deafness and Other Communication Disorders (R01 DC018579, to LSH)

iEEG · 148, 160, 106 ch512, 2048 HzBIDS 1.7.03 tasksEpilepsyAuditoryPerception

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 DS004993

dataset = DS004993(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 = DS004993(cache_dir="./data", subject="01")

Advanced query

dataset = DS004993(
    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{ds004993,
  title = {WIRED ICM Sample Dataset - Workshop on Intracranial Recordings in Humans, Epilepsy, DBS},
  author = {Liberty S. Hamilton and Maansi Desai and Alyssa Field},
  doi = {10.18112/openneuro.ds004993.v1.1.2},
  url = {https://doi.org/10.18112/openneuro.ds004993.v1.1.2},
}

§ 02Study · The README

About This Dataset#

Contributors: Liberty S. Hamilton, PhD, Maansi Desai, PhD, Alyssa Field, MEd

Email: liberty.hamilton@austin.utexas.edu This is a sample BIDS dataset for the WIRED ICM course in Paris, France in March 2024.

This contains intracranial recordings collected by the Hamilton Lab at the University of Texas at Austin. These recordings include examples of evoked data during natural listening tasks along with some examples of seizure-related activity and vagus nerve stimulator (VNS) artifact for illustrative purposes. All procedures were approved by the University of Texas at Austin Institutional Review Board.

Funding: Support was provided by the National Institutes of Health National Institute on Deafness and Other Communication Disorders (R01 DC018579, to LSH).

WIRED ICM TUTORIAL DATA

Tasks:

movietrailers - this task involves patients listening to movie clips from various Pixar, Disney, Dreamworks, and other movies. We have published previously using these stimuli in EEG (Desai et al. 2021).

timit4 and timit5 - these tasks involve patients listening to subsets of the TIMIT acoustic phonetic corpus (Garofolo et al 1993). The events provided in the dataset mark the onset and offset of each sentence. In timit4, each sentence is unique, while in timit5, 10 sentences are repeated 10 times. This is the same stimulus set used in Mesgarani et al. 2014, Hamilton et al. 2018, Hamilton et al. 2021, and Desai et. al 2021.

Notes:

* The movie trailer data for subject W1 was acquired at the start of a generalized tonic clonic seizure, and the research session was terminated. Large, synchronized spikes can be observed on multiple channels on the right parietal grid throughout the iEEG data. * The TIMIT data for subject W2 is an example of fairly clean sentence evoked data. * The TIMIT data for subject W3 is a good example of on-and-off VNS artifact. The VNS has a strong artifact at ~20 Hz. Some patients with epilepsy may have these implanted devices to help control their seizures, so you should know how to spot artifact-related activity. Despite these artifacts, the evoked responses to sentences are quite strong. * The acquisition number (B3, B8, etc) has to do with the order in which this task was run relative to other tasks in an iEEG session, and can be ignored here.

References

* Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 * Desai, M., Holder, J., Villarreal, C., Clark, N., Hoang, B., & Hamilton, L. S. (2021). Generalizable EEG encoding models with naturalistic audiovisual stimuli. Journal of Neuroscience, 41(43), 8946-8962. * Garofolo, J. S., Lamel, L. F., Fisher, W. M., Fiscus, J. G., & Pallett, D. S. (1993). DARPA TIMIT acoustic-phonetic continous speech corpus CD-ROM. NIST speech disc 1-1.1. NASA STI/Recon technical report n, 93, 27403. * Hamilton, L. S., Edwards, E., & Chang, E. F. (2018). A spatial map of onset and sustained responses to speech in the human superior temporal gyrus. Current Biology, 28(12), 1860-1871. * Hamilton, L. S., Oganian, Y., Hall, J., & Chang, E. F. (2021). Parallel and distributed encoding of speech across human auditory cortex. Cell, 184(18), 4626-4639. * Holdgraf, C., Appelhoff, S., Bickel, S., Bouchard, K., D’Ambrosio, S., David, O., … Hermes, D. (2019). iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Scientific Data, 6, 102. https://doi.org/10.1038/s41597-019-0105-7 * Mesgarani, N., Cheung, C., Johnson, K., & Chang, E. F. (2014). Phonetic feature encoding in human superior temporal gyrus. Science, 343(6174), 1006-1010.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=3, range 14–19 yr, mean 16.0 yr)

1015

Male · 3

Sex composition

Male
3

HandednessRight · 2Left · 1

Channel counts (ch)

106148160

Sampling frequencies (Hz)

5122048

Total recording duration: 13 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 148, 160, 106 ch · iEEG · 512, 2048 Hz · 3 subjects, 3 recordings

Live trace viewer — sub-W1 · ses-iemu · task-movietrailers · run-01

Showing one representative recording out of 3 subjects and 3 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 · 148 sensors — 148 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.DS004993(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Hamilton2024

Canonical—

Importable asDS004993 · Hamilton2024

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

class eegdash.dataset.DS004993(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

WIRED ICM Sample Dataset - Workshop on Intracranial Recordings in Humans, Epilepsy, DBS

Study:: ds004993 (OpenNeuro)
Author (year):: Hamilton2024
Canonical:: —

Also importable as: DS004993, Hamilton2024.

Modality: ieeg; Experiment type: Perception; Subject type: Epilepsy. Subjects: 3; recordings: 3; 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

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/ds004993 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004993 DOI: https://doi.org/10.18112/openneuro.ds004993.v1.1.2 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004993
>>> dataset = DS004993(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/ds004993 · pull with datasets.load_dataset("EEGDash/ds004993").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004993.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 ds004993 to reproduce the tutorial on this dataset.

Citation

Liberty S. Hamilton, Maansi Desai, Alyssa Field (2019). WIRED ICM Sample Dataset - Workshop on Intracranial Recordings in Humans, Epilepsy, DBS. 10.18112/openneuro.ds004993.v1.1.2

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds004993.v1.1.2.

Related & sibling datasets

DS005415IEEG · 13 subj DS004194IEEG · 14 subj DS003688IEEG · 51 subj DS004703IEEG · 10 subj DS005007IEEG · 40 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

events · channels · electrodes · coordsystem

Provenance

CC0 · 10.18112/openneuro.ds004993.v1.1.2

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004993`
Title	WIRED ICM Sample Dataset - Workshop on Intracranial Recordings in Humans, Epilepsy, DBS
Author (year)	`Hamilton2024`
Canonical	—
Importable as	`DS004993`, `Hamilton2024`
Year	2019
Authors	Liberty S. Hamilton, Maansi Desai, Alyssa Field
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004993.v1.1.2
Source links	OpenNeuro \| NeMAR \| Source URL

DS004993: ieeg dataset, 3 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#