EEGdash›NeMAR›NM000349

Iss. 349 · 39 subjects · 117 recordings · CC-BY-4.0

Dataset Brief · Chamanzar2020

NM000349: eeg dataset, 39 subjects#

Chamanzar2020: Ultra high-density EEG of interictal migraine and controls during visual, auditory stimulation and rest

Access recordings and metadata through EEGDash.

Citation: Alireza Chamanzar, Sarah M. Haigh, Pulkit Grover, Marlene Behrmann (2021). Chamanzar2020: Ultra high-density EEG of interictal migraine and controls during visual, auditory stimulation and rest. 10.82901/nemar.nm000349

Modality: eeg Subjects: 39 Recordings: 117 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

39-participant EEG dataset — Chamanzar2020: Ultra high-density EEG of interictal migraine and controls during visual, auditory stimulation and rest.

Data & curation Alireza Chamanzar · Sarah M. Haigh · Pulkit Grover · Marlene Behrmann
Senior author Marlene Behrmann
Year 2021 · Distributed via NeMAR
Funding CMU BrainHUB (MB, PG, AC) · Chuck Noll Foundation for Brain Injury Research (PG) · + 2 more

EEG · 144 ch512 HzBIDS 1.10.03 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 NM000349

dataset = NM000349(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 = NM000349(cache_dir="./data", subject="01")

Advanced query

dataset = NM000349(
    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{nm000349,
  title = {Chamanzar2020: Ultra high-density EEG of interictal migraine and controls during visual, auditory stimulation and rest},
  author = {Alireza Chamanzar and Sarah M. Haigh and Pulkit Grover and Marlene Behrmann},
  doi = {10.82901/nemar.nm000349},
  url = {https://doi.org/10.82901/nemar.nm000349},
}

§ 02Study · The README

About This Dataset#

BIDS conversion of the Carnegie Mellon University KiltHub release doi:10.1184/R1/12636731

(Chamanzar, Haigh, Grover, Behrmann; CC BY 4.0), data for Chamanzar et al. (2021), “Abnormalities in cortical pattern of coherence in migraine detected using ultra high-density EEG”, Brain Communications, doi:10.1093/braincomms/fcab061.

39 participants: 18 with migraine recorded interictally (sub-M01..sub-M18) and

21 healthy controls (sub-C01..sub-C21). The published analysis used 17 migraine (14 after excluding M2, M6, M18 for medication; M13 lacks the auditory task) and 18 matched controls; C2, C6 and C12 are additional controls without demographics. See participants.tsv (in_paper_analysis, notes).

DOI

Ultra high-density EEG of interictal migraine and controls: sensory and rest

Recording

128-channel BioSemi ActiveTwo, 512 Hz, 24-bit, custom ultra-high-density cap (~14 mm spacing) over occipital, parietal and frontal areas, inside a Faraday cage. Channel labels use 10-5 names, but the positions are custom and no digitized coordinates were released, so no electrodes.tsv is provided.

View full README

DOI

Ultra high-density EEG of interictal migraine and controls: sensory and rest

Recording

128-channel BioSemi ActiveTwo, 512 Hz, 24-bit, custom ultra-high-density cap (~14 mm spacing) over occipital, parietal and frontal areas, inside a Faraday cage. Channel labels use 10-5 names, but the positions are custom and no digitized coordinates were released, so no electrodes.tsv is provided.

Auxiliary channels: mastoids M1/M2 (type EEG), EOG LO1/LO2/IO1/SO1, ECG (collar bone), IO2 (role not documented, type MISC), GSR1/2, Erg1/2, Resp, Plet, Temp. Online reference CMS/DRL; no offline processing.

Mains frequency 60 Hz.

Tasks

task-ssvep: vertical grating flickering at 4 or 6 Hz for 2 s (100 trials each), ISI 1-1.5 s.
task-ssaep: 1 kHz tone amplitude-modulated at 4 or 6 Hz for 2 s (100 trials each), ISI 1-1.5 s.
task-rest: eyes open, fixation cross, 2-min blocks.

About 10% of task trials are attention trials with a key press (colour change of the fixation cross).

Event codes are decoded from the Status channel; see task-*_events.json. The Status channel’s bit 16 (value 65536) toggles occasionally; it is masked out and is not an event.

Known irregularities (from the original files)

sub-M13 has no SSAEP recording (documented in the release).
sub-C14 SSAEP was recorded in two files (run-1: 75+75 trials, run-2: 25+25 trials).
sub-C12 SSVEP is truncated (25+25 trials, ~215 s); not documented in the release.
Most resting files contain 2-3 blocks (~400 s) rather than the six blocks in the protocol; sub-M01 and sub-M02 contain ~800 s.
Trigger value 5 at the end of some resting files and one value 23 (sub-M03 SSVEP) are not described in the protocol; they are kept as undocumented_code_*.
Original file names: M15’s files are named P15_*; M1/M3 resting use M1resting/M3Resting.

Additional material

stimuli/: MATLAB/Psychtoolbox code of the three paradigms and SSVEP pattern files (from the release).
sourcedata/original_release/: the complete original KiltHub release, all 43 files byte-identical to doi:10.1184/R1/12636731 (per-subject zips, README.txt, protocol PDF, demographics sheet, stimulus zip).
sourcedata/sub-*/: per-subject feedback reaction-time files extracted from the zips (*aud_migraine.txt, *vis_migraine.txt: line 1 trial labels 1/2, line 2 attention trials, line 3 response times in s).
sourcedata/sourcedata_provenance.json: size, MD5/SHA-256 and origin of every source file; the BIDS *_eeg.bdf files are byte-identical to the BDFs inside the original zips.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000349-blue)](https://doi.org/10.82901/nemar.nm000349) # Ultra high-density EEG of interictal migraine and controls: sensory and rest BIDS conversion of the Carnegie Mellon University KiltHub release doi:10.1184/R1/12636731 (Chamanzar, Haigh, Grover, Behrmann; CC BY 4.0), data for Chamanzar et al. (2021), “Abnormalities in cortical pattern of coherence in migraine detected using ultra high-density EEG”, Brain Communications, doi:10.1093/braincomms/fcab061. ## Participants 39 participants: 18 with migraine recorded interictally (sub-M01..sub-M18) and 21 healthy controls (sub-C01..sub-C21). The published analysis used 17 migraine (14 after excluding M2, M6, M18 for medication; M13 lacks the auditory task) and 18 matched controls; C2, C6 and C12 are additional controls without demographics. See participants.tsv (in_paper_analysis, notes). ## Recording 128-channel BioSemi ActiveTwo, 512 Hz, 24-bit, custom ultra-high-density cap (~14 mm spacing) over occipital, parietal and frontal areas, inside a Faraday cage. Channel labels use 10-5 names, but the positions are custom and no digitized coordinates were released, so no electrodes.tsv is provided. Auxiliary channels: mastoids M1/M2 (type EEG), EOG LO1/LO2/IO1/SO1, ECG (collar bone), IO2 (role not documented, type MISC), GSR1/2, Erg1/2, Resp, Plet, Temp. Online reference CMS/DRL; no offline processing. Mains frequency 60 Hz. ## Tasks - task-ssvep: vertical grating flickering at 4 or 6 Hz for 2 s (100 trials each), ISI 1-1.5 s. - task-ssaep: 1 kHz tone amplitude-modulated at 4 or 6 Hz for 2 s (100 trials each), ISI 1-1.5 s. - task-rest: eyes open, fixation cross, 2-min blocks. About 10% of task trials are attention trials with a key press (colour change of the fixation cross). Event codes are decoded from the Status channel; see task-*_events.json. The Status channel’s bit 16 (value 65536) toggles occasionally; it is masked out and is not an event. ## Known irregularities (from the original files) - sub-M13 has no SSAEP recording (documented in the release). - sub-C14 SSAEP was recorded in two files (run-1: 75+75 trials, run-2: 25+25 trials). - sub-C12 SSVEP is truncated (25+25 trials, ~215 s); not documented in the release. - Most resting files contain 2-3 blocks (~400 s) rather than the six blocks in the protocol; sub-M01 and

sub-M02 contain ~800 s.

Trigger value 5 at the end of some resting files and one value 23 (sub-M03 SSVEP) are not described in the protocol; they are kept as undocumented_code_*.
Original file names: M15’s files are named P15_*; M1/M3 resting use M1resting/M3Resting.

## Additional material - stimuli/: MATLAB/Psychtoolbox code of the three paradigms and SSVEP pattern files (from the release). - sourcedata/original_release/: the complete original KiltHub release, all 43 files byte-identical to

doi:10.1184/R1/12636731 (per-subject zips, README.txt, protocol PDF, demographics sheet, stimulus zip).

sourcedata/sub-*/: per-subject feedback reaction-time files extracted from the zips (*aud_migraine.txt, *vis_migraine.txt: line 1 trial labels 1/2, line 2 attention trials, line 3 response times in s).
sourcedata/sourcedata_provenance.json: size, MD5/SHA-256 and origin of every source file; the BIDS *_eeg.bdf files are byte-identical to the BDFs inside the original zips.

License: CC-BY-4.0

Authors:

Alireza Chamanzar
Sarah M. Haigh
Pulkit Grover
Marlene Behrmann

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000349

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=36, range 19–54 yr, mean 27.6 yr)

15202530404550

Female · 26Male · 10

Sex composition

Female
26
Male
10
F : M ratio
2.60 : 1

72% female · n = 36 subjects with reported sex.

Channel counts: 144 ch (n=117 recordings)

Sampling frequencies: 512.0 Hz (n=117 recordings)

Total recording duration: 21 h 13 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 144 ch · EEG · 512 Hz · 39 subjects, 117 recordings

Live trace viewer — sub-C20 · task-ssaep

Showing one representative recording out of 39 subjects and 117 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 130 sensors — 130 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.NM000349(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000349

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

class eegdash.dataset.NM000349(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Chamanzar2020: Ultra high-density EEG of interictal migraine and controls during visual, auditory stimulation and rest

Study:: nm000349 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000349.

Modality: eeg; Subject type: Unknown. Subjects: 39; recordings: 117; 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/nm000349 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000349 DOI: https://doi.org/10.82901/nemar.nm000349

Examples

>>> from eegdash.dataset import NM000349
>>> dataset = NM000349(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 — NM000349.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 nm000349 to reproduce the tutorial on this dataset.

Citation

Alireza Chamanzar, Sarah M. Haigh, Pulkit Grover, Marlene Behrmann (2021). Chamanzar2020: Ultra high-density EEG of interictal migraine and controls during visual, auditory stimulation and rest. 10.82901/nemar.nm000349

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000349.

Related & sibling datasets

NM000312EEG · 27 subj NM000334EEG · 41 subj NM000318EEG · 20 subj NM000306EEG · 15 subj NM000280EEG · 47 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000349

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000349`
Title	Chamanzar2020: Ultra high-density EEG of interictal migraine and controls during visual, auditory stimulation and rest
Author (year)	—
Canonical	—
Importable as	`NM000349`
Year	2021
Authors	Alireza Chamanzar, Sarah M. Haigh, Pulkit Grover, Marlene Behrmann
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000349
Source links	OpenNeuro \| NeMAR \| Source URL

NM000349: eeg dataset, 39 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#