EEGdash›NeMAR›NM000501

Iss. 501 · 4 subjects · 20 recordings · CC-BY-4.0

Dataset Brief · ECoG_Tonal_Speech_Perception

NM000501: ieeg dataset, 4 subjects#

ECoG_Tonal_Speech_Perception

Access recordings and metadata through EEGDash.

Citation: Haobo Zhang, Daohan Zhang, Jinsong Wu, Yuanning Li, Junfeng Lu (2026). ECoG_Tonal_Speech_Perception. 10.82901/nemar.nm000501

Modality: ieeg Subjects: 4 Recordings: 20 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

4-participant iEEG dataset — ECoG_Tonal_Speech_Perception.

Data & curation Haobo Zhang · Daohan Zhang · Jinsong Wu · Yuanning Li · Junfeng Lu
Senior author Junfeng Lu
Year 2026 · Distributed via NeMAR

iEEG · 128 (12), 256 (8) ch400 HzBIDS 1.7.0Task · audio6 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#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000501

dataset = NM000501(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 = NM000501(cache_dir="./data", subject="01")

Advanced query

dataset = NM000501(
    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{nm000501,
  title = {ECoG_Tonal_Speech_Perception},
  author = {Haobo Zhang and Daohan Zhang and Jinsong Wu and Yuanning Li and Junfeng Lu},
  doi = {10.82901/nemar.nm000501},
  url = {https://doi.org/10.82901/nemar.nm000501},
}

§ 02Study · The README

About This Dataset#

Huashan tonal speech ECoG (Zhang et al. Sci Data 2026), BIDS ingest.

SIGNAL IS HIGH-GAMMA ENVELOPE (70-150 Hz Hilbert), not raw voltage.

Source NWB iEEG series + *_ieeg.json document the envelope derivation.

EDF stores envelope in uV; round-trip max error per file in convert_report.json.

DOI

One misnamed duplicate NWB (sub-01 ses-05 run-05) excluded; requirements.txt excluded.

Ethics

Zhang et al. (2026), Methods (verbatim):

The study was approved by the Huashan Hospital Institutional Review Board of Fudan University (HIRB, KY2017-437), and the conducting of research complied with all relevant ethical considerations.

Participants signed their names on informed consent the day before surgery and were told that the task was only for research purposes, which could be terminated at any time during the task.

Source: Zhang H, Zhang D, Wu J, Li Y, Lu J, et al. (2026). A Fine-grained Spatiotemporal ECoG Dataset during Speech Perception in Tonal Language. Scientific Data. https://doi.org/10.1038/s41597-026-07619-z (PMC13635214)

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000501-blue)](https://doi.org/10.82901/nemar.nm000501) Huashan tonal speech ECoG (Zhang et al. Sci Data 2026), BIDS ingest. SIGNAL IS HIGH-GAMMA ENVELOPE (70-150 Hz Hilbert), not raw voltage. Source NWB iEEG series + *_ieeg.json document the envelope derivation. EDF stores envelope in uV; round-trip max error per file in convert_report.json. One misnamed duplicate NWB (sub-01 ses-05 run-05) excluded; requirements.txt excluded. ## Ethics Zhang et al. (2026), Methods (verbatim): > The study was approved by the Huashan Hospital Institutional Review Board of Fudan University (HIRB, KY2017-437), and the conducting of research complied with all relevant ethical considerations. > > Participants signed their names on informed consent the day before surgery and were told that the task was only for research purposes, which could be terminated at any time during the task. Source: Zhang H, Zhang D, Wu J, Li Y, Lu J, et al. (2026). A Fine-grained Spatiotemporal ECoG Dataset during Speech Perception in Tonal Language. Scientific Data. https://doi.org/10.1038/s41597-026-07619-z (PMC13635214)

License: CC-BY-4.0

Authors:

Haobo Zhang
Daohan Zhang
Jinsong Wu
Yuanning Li
Junfeng Lu

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000501

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=4, range 34–55 yr, mean 46.8 yr)

30455055

Other · 4

Sex composition

Female
2
Male
2
F : M ratio
1.00 : 1

50% female · n = 4 subjects with reported sex.

HandednessRight · 4

Channel counts (ch)

128256

Sampling frequencies: 400.0 Hz (n=20 recordings)

Total recording duration: 1 h 20 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 128 (12), 256 (8) ch · iEEG · 400 Hz · 4 subjects, 20 recordings

Live trace viewer — sub-04 · ses-03 · task-audio

Showing one representative recording out of 4 subjects and 20 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 · 128 sensors — 128 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.NM000501(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000501

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

class eegdash.dataset.NM000501(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

ECoG_Tonal_Speech_Perception

Study:: nm000501 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000501.

Modality: ieeg; Subject type: Unknown. Subjects: 4; recordings: 20; 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/nm000501 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000501 DOI: https://doi.org/10.82901/nemar.nm000501

Examples

>>> from eegdash.dataset import NM000501
>>> dataset = NM000501(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 — NM000501.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 nm000501 to reproduce the tutorial on this dataset.

Citation

Haobo Zhang, Daohan Zhang, Jinsong Wu, Yuanning Li, Junfeng Lu (2026). ECoG_Tonal_Speech_Perception. 10.82901/nemar.nm000501

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000501.

Related & sibling datasets

NM000409IEEG · 8 subj NM000437IEEG · 5 subj NM000472IEEG · 1 subj NM000400IEEG · 7 subj NM000513IEEG · 7 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

events · events.json · channels · electrodes · coordsystem · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000501

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000501`
Title	ECoG_Tonal_Speech_Perception
Author (year)	—
Canonical	—
Importable as	`NM000501`
Year	2026
Authors	Haobo Zhang, Daohan Zhang, Jinsong Wu, Yuanning Li, Junfeng Lu
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000501
Source links	OpenNeuro \| NeMAR \| Source URL

NM000501: ieeg dataset, 4 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#