NM000110: eeg dataset, 24 subjects#

CHB-MIT

Access recordings and metadata through EEGDash.

Citation: Jack Connolly, Herman Edwards, Blaise Bourgeois, S. Ted Treves, Ali Shoeb, John Guttag (2010). CHB-MIT. 10.82901/nemar.nm000110

Modality: eeg Subjects: 24 Recordings: 686 License: ODC-By-1.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000110

dataset = NM000110(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000110(cache_dir="./data", subject="01")

Advanced query

dataset = NM000110(
    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{nm000110,
  title = {CHB-MIT},
  author = {Jack Connolly and Herman Edwards and Blaise Bourgeois and S. Ted Treves and Ali Shoeb and John Guttag},
  doi = {10.82901/nemar.nm000110},
  url = {https://doi.org/10.82901/nemar.nm000110},
}

About This Dataset#

DOI

CHB-MIT

Introduction

The CHB-MIT Scalp EEG Database consists of EEG recordings from pediatric subjects with intractable seizures. This dataset was collected at the Children’s Hospital Boston and includes recordings from 22 subjects (5 males, ages 3-22; and 17 females, ages 1.5-19) with epilepsy. The recordings contain 198 annotated seizures and were originally collected to characterize seizures and assess patients’ candidacy for surgical intervention.

Overview of the experiment

View full README

DOI

CHB-MIT

Introduction

The CHB-MIT Scalp EEG Database consists of EEG recordings from pediatric subjects with intractable seizures. This dataset was collected at the Children’s Hospital Boston and includes recordings from 22 subjects (5 males, ages 3-22; and 17 females, ages 1.5-19) with epilepsy. The recordings contain 198 annotated seizures and were originally collected to characterize seizures and assess patients’ candidacy for surgical intervention.

Overview of the experiment

Subjects were monitored for up to several days following withdrawal of anti-seizure medication in a controlled hospital environment. The purpose was to capture and characterize their seizure patterns using continuous scalp EEG monitoring. Each case (subject) contains between 9 and 42 continuous EEG recording files. All signals were sampled at 256 samples per second with 16-bit resolution. Most files contain 23 EEG signals recorded using the International 10-20 system of EEG electrode positions and nomenclature. The recordings use bipolar montages, where each channel represents the potential difference between two electrode sites. Hardware limitations resulted in gaps between consecutively-numbered files, typically 10 seconds or less, during which signals were not recorded. Most recording files contain exactly one hour of digitized EEG signals, though some cases contain two-hour or four-hour recordings. Additional signals such as ECG and vagal nerve stimulus (VNS) were recorded in some cases.

Description of the preprocessing if any

The original .edf files from PhysioNet have been converted to BIDS format. Channel names have been standardized to match the standard 10-05 montage naming convention. Bipolar channel pairs are represented in the format “Electrode1-Electrode2” (e.g., “FP1-F7”). Non-EEG channels such as ECG are preserved with appropriate BIDS channel types. Channels that did not match expected formats or could not be mapped to the standard montage were marked as “misc” type. All protected health information (PHI) in the original files has been replaced with surrogate information. Dates have been replaced with surrogate dates while preserving time relationships between files. Subject birthdates are calculated based on age at recording time when available.

Description of the event values if any

The events.tsv files contain seizure onset and offset annotations. Each seizure event has: - onset: Time in seconds from the beginning of the recording when the seizure starts - duration: Duration of the seizure in seconds - value: “seizure” - indicating a seizure event - sample: Sample number at onset

The seizure annotations were originally marked with ‘[’ for onset and ‘]’ for offset in the .seizures annotation files and have been converted to BIDS-compliant event format. In total, the dataset contains 198 seizure events across all subjects (182 in the original 23 cases, plus 16 additional seizures from case chb24 added in December 2010).

Citation

When using this dataset, please cite: 1. Ali Shoeb. Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. PhD Thesis, Massachusetts Institute of Technology, September 2009. http://hdl.handle.net/1721.1/54669 2. Guttag, J. (2010). CHB-MIT Scalp EEG Database (version 1.0.0). PhysioNet. https://doi.org/10.13026/C2K01R 3. Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., … & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

Data curators: Pierre Guetschel (BIDS conversion) Original data collection team: - Jack Connolly, REEGT (Children’s Hospital Boston) - Herman Edwards, REEGT (Children’s Hospital Boston) - Blaise Bourgeois, MD (Children’s Hospital Boston) - S. Ted Treves, MD (Children’s Hospital Boston) - Ali Shoeb, PhD (Massachusetts Institute of Technology)

- Professor John Guttag (Massachusetts Institute of Technology)

Automatic report

Report automatically generated by ``mne_bids.make_report()``.

The CHB-MIT dataset was created by Jack Connolly, Herman Edwards, Blaise

Bourgeois, S. Ted Treves, Ali Shoeb, and John Guttag and conforms to BIDS version 1.7.0. This report was generated with MNE-BIDS (https://doi.org/10.21105/joss.01896). The dataset consists of 24 participants (comprised of 5 male and 18 female participants; handedness were all unknown; ages ranged from 71.0 to 91.0 (mean = 79.04, std = 5.51; 1 with unknown age)) . Data was recorded using an EEG system sampled at 256.0 Hz with line noise at n/a Hz. There were 686 scans in total. Recording durations ranged from 600.0 to 14427.0 seconds (mean = 5158.26, std = 3657.58), for a total of 3538564.32 seconds of data recorded over all scans. For each dataset, there were on average 26.03 (std = 3.81) recording channels per scan, out of which 26.03 (std = 3.81) were used in analysis (0.0 +/- 0.0 were removed from analysis).

Dataset Information#

Dataset ID

NM000110

Title

CHB-MIT

Author (year)

Connolly2010

Canonical

CHBMIT, CHB_MIT

Importable as

NM000110, Connolly2010, CHBMIT, CHB_MIT

Year

2010

Authors

Jack Connolly, Herman Edwards, Blaise Bourgeois, S. Ted Treves, Ali Shoeb, John Guttag

License

ODC-By-1.0

Citation / DOI

10.82901/nemar.nm000110

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000110,
  title = {CHB-MIT},
  author = {Jack Connolly and Herman Edwards and Blaise Bourgeois and S. Ted Treves and Ali Shoeb and John Guttag},
  doi = {10.82901/nemar.nm000110},
  url = {https://doi.org/10.82901/nemar.nm000110},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 24

  • Recordings: 686

  • Tasks: 1

Channels & sampling rate

  • Channels: 23 (306), 28 (259), 38 (39), 22 (36), 24 (30), 29 (14), 25, 31

  • Sampling rate (Hz): 256

  • Duration (hours): 982.9345334201388

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 42.6 GB

  • File count: 686

  • Format: BIDS

License & citation

  • License: ODC-By-1.0

  • DOI: 10.82901/nemar.nm000110

Provenance

API Reference#

Use the NM000110 class to access this dataset programmatically.

class eegdash.dataset.NM000110(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

CHB-MIT

Study:

nm000110 (NeMAR)

Author (year):

Connolly2010

Canonical:

CHBMIT, CHB_MIT

Also importable as: NM000110, Connolly2010, CHBMIT, CHB_MIT.

Modality: eeg. Subjects: 24; recordings: 686; 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/nm000110 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000110 DOI: https://doi.org/10.82901/nemar.nm000110

Examples

>>> from eegdash.dataset import NM000110
>>> dataset = NM000110(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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#