EEGdash›NeMAR›NM000430
Iss. 430 · 2 subjects · 8 recordings · CC-BY-4.0
Dataset Brief · Intracranial recordings before and after left anterior tempor…

NM000430: ieeg dataset, 2 subjects#

Intracranial recordings before and after left anterior temporal lobe disconnection during a speech prediction task (Kocsis et al., 2023) - derivative

Access recordings and metadata through EEGDash.

Citation: Zsuzsanna Kocsis, Rick L. Jenison, Peter N. Taylor, Ryan M. Calmus, Bob McMurray, Ariane E. Rhone, McCall E. Sarrett, Carolina Deifelt Streese, Yukiko Kikuchi, Phillip E. Gander, Joel I. Berger, Christopher K. Kovach, Inyong Choi, Jeremy D. Greenlee, Hiroto Kawasaki, Thomas E. Cope, Timothy D. Griffiths, Matthew A. Howard III, Christopher I. Petkov (2023). Intracranial recordings before and after left anterior temporal lobe disconnection during a speech prediction task (Kocsis et al., 2023) - derivative. 10.82901/nemar.nm000430

Modality: ieeg Subjects: 2 Recordings: 8 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

2-participant iEEG dataset — Intracranial recordings before and after left anterior temporal lobe disconnection during a speech prediction task (Kocsis et al., 2023) - derivative.

Data & curation Zsuzsanna Kocsis · Rick L. Jenison · Peter N. Taylor · Ryan M. Calmus · Bob McMurray · Ariane E. Rhone · …
Senior author Christopher I. Petkov
Year 2023 · Distributed via NeMAR
Funding Medical Research Council MR/X003701/1 · Academy of Medical Sciences SGL024\1069 · + 3 more
iEEG · 128 ch1000 HzBIDS 1.10.0Task · speechprediction2 sessions
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000430

dataset = NM000430(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000430(cache_dir="./data", subject="01")

Advanced query

dataset = NM000430(
    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{nm000430,
  title = {Intracranial recordings before and after left anterior temporal lobe disconnection during a speech prediction task (Kocsis et al., 2023) - derivative},
  author = {Zsuzsanna Kocsis and Rick L. Jenison and Peter N. Taylor and Ryan M. Calmus and Bob McMurray and Ariane E. Rhone and McCall E. Sarrett and Carolina Deifelt Streese and Yukiko Kikuchi and Phillip E. Gander and Joel I. Berger and Christopher K. Kovach and Inyong Choi and Jeremy D. Greenlee and Hiroto Kawasaki and Thomas E. Cope and Timothy D. Griffiths and Matthew A. Howard III and Christopher I. Petkov},
  doi = {10.82901/nemar.nm000430},
  url = {https://doi.org/10.82901/nemar.nm000430},
}
§ 02Study · The README

About This Dataset#

Acute electrocorticography from two epilepsy surgery patients (University of Iowa) recorded during a speech prediction task

minutes before (ses-pre) and after (ses-post) neurosurgical disconnection of the left anterior temporal lobe (ATL).

Article: left hemisphere; frontal grid (96 contacts P1, 64 contacts P2), temporal pole, subtemporal and lateral temporal

strips, an 8-contact Heschl’s gyrus depth electrode; two subgaleal electrodes as ground and reference; anaesthesia stopped at least 20 min before each recording. THIS IS A DERIVATIVE DATASET: the release’s “raw” files are already downsampled from 2000 Hz to 1000 Hz (article: “sampled at 2000 Hz … then downsampled to 1000 Hz”), so they are labelled desc-downsampled; the denoised files are desc-dbtsvd (demodulated band transform + removal of the first SVD component).

DOI

Intracranial recordings before and after semantic hub (left ATL) disconnection (Kocsis et al., 2023) - DERIVATIVE

Source

  • Zenodo: Zsuzsanna Kocsis, Rick L. Jenison, Peter N. Taylor et al. doi:10.5281/zenodo.8110724 (2023-07-05). License CC-BY-4.0.

  • Article: Nat Commun 14 (2023), doi:10.1038/s41467-023-42088-7 (PMC10560235).

  • All 14 release files were downloaded from Zenodo on Voyager (md5 verified) and are in sourcedata/zenodo-8110724/ unchanged.

View full README

DOI

Intracranial recordings before and after semantic hub (left ATL) disconnection (Kocsis et al., 2023) - DERIVATIVE

Source

  • Zenodo: Zsuzsanna Kocsis, Rick L. Jenison, Peter N. Taylor et al. doi:10.5281/zenodo.8110724 (2023-07-05). License CC-BY-4.0.

  • Article: Nat Commun 14 (2023), doi:10.1038/s41467-023-42088-7 (PMC10560235).

  • All 14 release files were downloaded from Zenodo on Voyager (md5 verified) and are in sourcedata/zenodo-8110724/ unchanged.

Contents

  • sub-0<p>/ses-<pre|post>/ieeg/*_task-speechprediction_desc-<downsampled|dbtsvd>_ieeg.*: 128 channels at 1000 Hz, 1.92 h per desc in total. Units are not stated in the release (n/a). Values float64 -> float32.

  • Channel names from Information_sheet.xlsx (grid N, TemporalPole N, SubtemporalStrip N, LateralTemporalStrip N, HGdepth N). Matrix channels not listed in the sheet are named chNNN, type MISC: ch085, ch086, ch087, ch088, ch089, ch090, ch091, ch092, ch093, ch094, ch095, ch096, ch097, ch098, ch099, ch100, ch101, ch102, ch103, ch104, ch105, ch106, ch107, ch108, ch109, ch110, ch111, ch112, ch113, ch114, ch115, ch116, ch117, ch118, ch119, ch120, ch121, ch122, ch123, ch124, ch125, ch126, ch127, ch128.

  • events.tsv: event code and sample from the *_events.mat files; meanings from the sheet ‘event codes’ (biasing sentence onset; target word onset with B/P bias and VOT 0-40 ms; filler trials).

Localisation

electrodes.tsv per session: MNI coordinates (mm) and Desikan-Killiany / Destrieux labels per contact, as listed in the release sheet for each period (the frontal grid positions differ pre/post; article: displacement was checked and corrected).

The MNI template is not named, so coordsystem.json says Other with that description. Temporal pole and subtemporal strip contacts have no coordinates in the release. Sheet notes: P1: sheet has 2 location tables, both headed ‘Pre-disconnection locations’; the second is taken as post-disconnection (frontal grid coordinates differ; article: grid displacement after disconnection was corrected) | P1 pre: channel numbers in the sheet are not consecutive ([113]…); mapped by row order starting at 1 | P2: sheet has 2 location tables, both headed ‘Pre-disconnection locations’; the second is taken as post-disconnection (frontal grid coordinates differ; article: grid displacement after disconnection was corrected) | P2 pre: channel numbers in the sheet are not consecutive ([97, 98, 99, 100, 101]…); mapped by row order starting at 1 | P2 post: channel numbers in the sheet are not consecutive ([97, 98, 99, 100, 101]…); mapped by row order starting at 1

Privacy

The release holds participant codes P1/P2, signals, event codes and contact tables; age and sex come from the article.

String scan of the event files: 4 flagged strings.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000430-blue)](https://doi.org/10.82901/nemar.nm000430) # Intracranial recordings before and after semantic hub (left ATL) disconnection (Kocsis et al., 2023) - DERIVATIVE Acute electrocorticography from two epilepsy surgery patients (University of Iowa) recorded during a speech prediction task minutes before (ses-pre) and after (ses-post) neurosurgical disconnection of the left anterior temporal lobe (ATL). Article: left hemisphere; frontal grid (96 contacts P1, 64 contacts P2), temporal pole, subtemporal and lateral temporal strips, an 8-contact Heschl’s gyrus depth electrode; two subgaleal electrodes as ground and reference; anaesthesia stopped at least 20 min before each recording. THIS IS A DERIVATIVE DATASET: the release’s “raw” files are already downsampled from 2000 Hz to 1000 Hz (article: “sampled at 2000 Hz … then downsampled to 1000 Hz”), so they are labelled desc-downsampled; the denoised files are desc-dbtsvd (demodulated band transform + removal of the first SVD component). ## Source - Zenodo: Zsuzsanna Kocsis, Rick L. Jenison, Peter N. Taylor et al. doi:10.5281/zenodo.8110724 (2023-07-05). License CC-BY-4.0. - Article: Nat Commun 14 (2023), doi:10.1038/s41467-023-42088-7 (PMC10560235). - All 14 release files were downloaded from Zenodo on Voyager (md5 verified) and are in sourcedata/zenodo-8110724/ unchanged. ## Contents - sub-0<p>/ses-<pre|post>/ieeg/*_task-speechprediction_desc-<downsampled|dbtsvd>_ieeg.*: 128 channels at 1000 Hz,

1.92 h per desc in total. Units are not stated in the release (n/a). Values float64 -> float32.

  • Channel names from Information_sheet.xlsx (grid N, TemporalPole N, SubtemporalStrip N, LateralTemporalStrip N, HGdepth N). Matrix channels not listed in the sheet are named chNNN, type MISC: ch085, ch086, ch087, ch088, ch089, ch090, ch091, ch092, ch093, ch094, ch095, ch096, ch097, ch098, ch099, ch100, ch101, ch102, ch103, ch104, ch105, ch106, ch107, ch108, ch109, ch110, ch111, ch112, ch113, ch114, ch115, ch116, ch117, ch118, ch119, ch120, ch121, ch122, ch123, ch124, ch125, ch126, ch127, ch128.

  • events.tsv: event code and sample from the *_events.mat files; meanings from the sheet ‘event codes’ (biasing sentence onset; target word onset with B/P bias and VOT 0-40 ms; filler trials).

## Localisation electrodes.tsv per session: MNI coordinates (mm) and Desikan-Killiany / Destrieux labels per contact, as listed in the release sheet for each period (the frontal grid positions differ pre/post; article: displacement was checked and corrected). The MNI template is not named, so coordsystem.json says Other with that description. Temporal pole and subtemporal strip contacts have no coordinates in the release. Sheet notes: P1: sheet has 2 location tables, both headed ‘Pre-disconnection locations’; the second is taken as post-disconnection (frontal grid coordinates differ; article: grid displacement after disconnection was corrected) | P1 pre: channel numbers in the sheet are not consecutive ([113]…); mapped by row order starting at 1 | P2: sheet has 2 location tables, both headed ‘Pre-disconnection locations’; the second is taken as post-disconnection (frontal grid coordinates differ; article: grid displacement after disconnection was corrected) | P2 pre: channel numbers in the sheet are not consecutive ([97, 98, 99, 100, 101]…); mapped by row order starting at 1 | P2 post: channel numbers in the sheet are not consecutive ([97, 98, 99, 100, 101]…); mapped by row order starting at 1 ## Privacy The release holds participant codes P1/P2, signals, event codes and contact tables; age and sex come from the article. String scan of the event files: 4 flagged strings.

License: CC-BY-4.0

Authors:

  • Zsuzsanna Kocsis

  • Rick L. Jenison

  • Peter N. Taylor

  • Ryan M. Calmus

  • Bob McMurray

  • … and 14 more

Versions:

Version

DOI

Released

current

10.82901/nemar.nm000430
§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=2, range 32–63 yr, mean 47.5 yr)

3060
Female · 1Male · 1

Sex composition

2
subjects
Female
1
Male
1
F : M ratio
1.00 : 1
50% female · n = 2 subjects with reported sex.
HandednessRight · 2

Channel counts: 128 ch (n=8 recordings)

Sampling frequencies: 1000.0 Hz (n=8 recordings)

Total recording duration: 3 h 50 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 128 ch · iEEG · 1000 Hz · 2 subjects, 8 recordings
Live trace viewer — sub-01 · ses-pre · task-speechprediction

Showing one representative recording out of 2 subjects and 8 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 · 108 sensors — 108 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.

NEMAR pipeline statistics — NM000430
HED event descriptors word cloud HED event descriptors word cloud — NM000430
§ 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…
Full dataset metadata table

Dataset ID

NM000430

Title

Intracranial recordings before and after left anterior temporal lobe disconnection during a speech prediction task (Kocsis et al., 2023) - derivative

Author (year)

—

Canonical

—

Importable as

NM000430

Year

2023

Authors

Zsuzsanna Kocsis, Rick L. Jenison, Peter N. Taylor, Ryan M. Calmus, Bob McMurray, Ariane E. Rhone, McCall E. Sarrett, Carolina Deifelt Streese, Yukiko Kikuchi, Phillip E. Gander, Joel I. Berger, Christopher K. Kovach, Inyong Choi, Jeremy D. Greenlee, Hiroto Kawasaki, Thomas E. Cope, Timothy D. Griffiths, Matthew A. Howard III, Christopher I. Petkov

License

CC-BY-4.0

Citation / DOI

10.82901/nemar.nm000430

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000430,
  title = {Intracranial recordings before and after left anterior temporal lobe disconnection during a speech prediction task (Kocsis et al., 2023) - derivative},
  author = {Zsuzsanna Kocsis and Rick L. Jenison and Peter N. Taylor and Ryan M. Calmus and Bob McMurray and Ariane E. Rhone and McCall E. Sarrett and Carolina Deifelt Streese and Yukiko Kikuchi and Phillip E. Gander and Joel I. Berger and Christopher K. Kovach and Inyong Choi and Jeremy D. Greenlee and Hiroto Kawasaki and Thomas E. Cope and Timothy D. Griffiths and Matthew A. Howard III and Christopher I. Petkov},
  doi = {10.82901/nemar.nm000430},
  url = {https://doi.org/10.82901/nemar.nm000430},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.NM000430(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)—
Canonical—
Importable asNM000430
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.NM000430(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Intracranial recordings before and after left anterior temporal lobe disconnection during a speech prediction task (Kocsis et al., 2023) - derivative

Study:

nm000430 (NeMAR)

Author (year):

—

Canonical:

—

Also importable as: NM000430.

Modality: ieeg; Subject type: Unknown. Subjects: 2; recordings: 8; 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/nm000430 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000430 DOI: https://doi.org/10.82901/nemar.nm000430

Examples

>>> from eegdash.dataset import NM000430
>>> dataset = NM000430(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 — NM000430.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 nm000430 to reproduce the tutorial on this dataset.

Citation

Zsuzsanna Kocsis, Rick L. Jenison, Peter N. Taylor, Ryan M. Calmus, Bob McMurray, … (2023). Intracranial recordings before and after left anterior temporal lobe disconnection during a speech prediction task (Kocsis et al., 2023) - derivative. 10.82901/nemar.nm000430

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000430.

BIDS
BIDS 1.10.0
Sidecars
events · channels · eeg.json
Provenance
CC-BY-4.0 · 10.82901/nemar.nm000430
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#