EEGdash›OpenNeuro›DS005305
Iss. 5305 · 165 subjects · 165 recordings · CC0
Dataset Brief · EEG Resting-state Microstates Correlates of Executive Functions

DS005305: eeg dataset, 165 subjects#

EEG Resting-state Microstates Correlates of Executive Functions

Citation: Chenot Quentin, Hamery Caroline, Truninger Moritz, De Boissezon Xavier, Langer Nicolas, Scannella Sébastien (20). EEG Resting-state Microstates Correlates of Executive Functions. 10.18112/openneuro.ds005305.v1.0.1

165-participant EEG dataset — EEG Resting-state Microstates Correlates of Executive Functions.

Data & curation Chenot Quentin · Hamery Caroline · Truninger Moritz · De Boissezon Xavier · Langer Nicolas · Scannella Sébastien
Year 20 · Distributed via OpenNeuro
EEG · 64 ch512 Hz · mixedBIDS 1.8.0Task · restingstateHealthyVisualDecision-making
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 DS005305

dataset = DS005305(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS005305(cache_dir="./data", subject="01")

Advanced query

dataset = DS005305(
    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{ds005305,
  title = {EEG Resting-state Microstates Correlates of Executive Functions},
  author = {Chenot Quentin and Hamery Caroline and Truninger Moritz and De Boissezon Xavier and Langer Nicolas and Scannella Sébastien},
  doi = {10.18112/openneuro.ds005305.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005305.v1.0.1},
}
§ 02Study · The README

About This Dataset#

Project name: Project Microstates & Executive Functions (Project_microstates_EFs)

Years: 2021-2023 Contact person: Quentin chenot (quentinchenot@gmail.com)

Summary: This study aimed to specifically explore the relationship between intrinsic brain spatio-temporal dynamics and Executive Functions.

README

To do so, resting-state EEG microstates were used to assess brain spatio-temporal dynamics in 140 healthy participants, while a comprehensive battery of nine cognitive function tasks was employed to evaluate their executive functions. Correlations were computed between the EEG microstates metrics at rest and the mean score in executive function tasks.

Dataset content

1160 Files, 6.38GB 165 - Subjects 2 - Session

View full README

README

To do so, resting-state EEG microstates were used to assess brain spatio-temporal dynamics in 140 healthy participants, while a comprehensive battery of nine cognitive function tasks was employed to evaluate their executive functions. Correlations were computed between the EEG microstates metrics at rest and the mean score in executive function tasks.

Dataset content

1160 Files, 6.38GB 165 - Subjects 2 - Session Available Tasks: Resting-State, Antisaccade, Category-Switch, Color-Shape, Dual N-back, Keep-Track, Letter-Memory, Number-Letter, Stop-Signal, Stroop Available Modalities: EEG Independent variables: NA Dependent variables:

DV1: Mean score in the nine executive functions tasks (z-scored) DV2: resting-state EEG microstates metrics (number of occurrences, mean duration)

Control variables:

Age Gender Education Handedness

Methods

Subjects

165 participants were recruited for this experiment. 140 constitute the final sample.

Recrutment procedure: participants were recruited with flyers, mailing-lists and mouth to hear in the Toulouse University campuses. Inclusion criteria: age (18-35 years); affiliation to social insurance; having read the information document about the experiment; signed informed consent form; native French language Exclusion criteria: addiction (alcohol, drugs); major hearing loss; major visual deficit; including hemianopsia and color blindness; neurological or psychiatric pathology; known brain injury, drugs intake targeting the central nervous system; refusal to sign the consent form

Material

Participants performed the experiment in a windowless room at a stable temperature, seated and facing a 24’ inches screen. They underwent two sessions: session 1: EEG measures with a 5 min resting-state alternating between eyes closed and eyes open (30 sec each). EEG apparatus: 64 electrodes Biosemi Active-two amplifier (data acquired at 512 Hz) session 2: behavioral measures, with the nine cognitive tasks in the following order: antisaccade, letter-memory, color–shape, number–letter, Stroop, keep track, dual n-back, category switch, stop-signal.

Experimental location and acquisition timeframe

The experiment took place in the Centre de Neuroergonomie at ISAE-SUPAERO (Toulouse, France), from february 2022 to july 2023.

Installation and Setup

The repository contains scripts for preprocessing and analyzing behavioral and EEG data, as well as the project’s documentation and results.

See code_documentation.pdf in docs (available in https://osf.io/fm58p/).

Publications

Registered Report stage 1 IPA: EEG resting-state microstates correlates of executive functions (https://osf.io/dwz2r) Registered Report stage 2: Investigating the relationship between Resting-State EEG Microstates and Executive Functions: A Null Finding (https://doi.org/10.1016/j.cortex.2024.05.019)

Authors

Quentin Chenot, Caroline Hamery, Moritz Truninger, Xavier De Boissezon, Nicolas Langer, Sébastien Scannella

CRediT author statement

QC: conceptualization, methodology, software, formal analysis, investigation, data curation, writing – original draft, writing – review & editing, Visualization. CH: methodology, software, validation, formal analysis, data curation, writing – review & editing, visualization. MT: methodology, software, validation, formal analysis, data curation, writing – review & editing, visualization. NL: methodology, software, validation, formal analysis, resources, writing – review & editing. XDB: resources, writing – review & editing, supervision, funding acquisition. SS: conceptualization, methodology, resources, writing – review & editing, supervision, project administration, funding acquisition.

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Funding

This work is supported by the French National Research Agency (ANR) and the Defense Procurement Agency (DGA), ASTRID program [grant numbers ANR-17-ASTR-0005]

Notes - Participant and sessions timeframe

Participant Session 1 Session 2 (DD/MM/YYYY - HH) sub-001 21/02/2022 - 10h 24/02/2022 - 14h sub-002 21/02/2022 - 17h 01/03/2022 - 17h sub-003 22/02/2022 - 8h 28/02/2022 - 11h sub-004 22/02/2022 - 13h 01/03/2022 - 9h sub-005 08/03/2022 - 9h 14/03/2022 - 9h15 sub-006 08/03/2022 - 13h 10/03/2022 - 13h30 sub-007 08/03/2022 - 15h30 16/03/2022 - 9h sub-008 09/03/2022 - 13h 11/03/2022 - 10h sub-009 09/03/2022 - 17h 17/03/2022 - 10h30 sub-010 10/03/2022 - 13h 11/03/2022 - 13h sub-011 10/03/2022 - 19h 17/03/2022 - 18h30 sub-012 14/03/2022 - 8h 21/03/2022 - 8h30 sub-013 14/03/2022 - 17h 18/03/2022 - 17h sub-014 15/03/2022 - 8h 18/03/2022 - 14h30 sub-015 15/03/2022 - 13h 21/03/2022 - 15h30 sub-016 16/03/2022 - 8h 23/03/2022 - 8h sub-017 16/03/2022 - 13h 22/03/2022 - 15h00 sub-018 16/03/2022 - 15h30 22/03/2022 - 9h sub-019 17/03/2022 - 8h 25/03/2022 - 8h15 sub-020 17/03/2022 - 16h30 24/03/2022 - 14h sub-021 18/03/2022 - 9h 22/03/2022 - 13h sub-022 18/03/2022 - 13h 24/03/2022 - 11h30 sub-023 18/03/2022 - 16h 22/03/2022 - 17h sub-024 21/03/2022 - 16h 28/03/2022 - 16h sub-025 22/03/2022 - 9h 28/03/2022 - 9h sub-026 22/03/2022 - 13h 25/03/2022 - 15h sub-027 23/03/2022 - 18h 30/03/2022 - 18h sub-028 24/03/2022 - 17h 25/03/2022 - 13h sub-029 25/03/2022 - 13h 31/03/2022 - 17h30 sub-030 28/03/2022 - 9h 31/03/2022 - 9h sub-031 29/03/2022 - 17h 31/03/2022 - 13h sub-032 30/03/2022 - 17h 01/04/2022 - 14h sub-033 04/04/2022 - 9h 05/04/2022 - 14h sub-034 04/04/2022 - 13h 05/04/2022 - 10h sub-035 04/04/2022 - 16h 11/04/2022 - 16h sub-036 06/04/2022 - 9h 08/04/2022 - 9h sub-037 07/04/2022 - 15h 08/04/2022 - 13h sub-038 08/04/2022 - 13h 25/04/2022 - 9h sub-039 12/04/2022 - 9h 15/04/2022 - 14h sub-040 14/04/2022 - 9h 25/04/2022 - 17h sub-041 15/04/2022 - 13h 27/04/2022 - 14h sub-042 19/04/2022 - 16h 20/04/2022 - 16h sub-043 20/04/2022 - 9h 21/04/2022 - 9h sub-044 21/04/2022 - 13h 22/04/2022 - 17h sub-045 21/04/2022 - 16h 28/04/2022 - 17h sub-046 22/04/2022 - 16h 27/04/2022 - 17h sub-047 25/04/2022 - 9h 28/04/2022 - 10h sub-048 25/04/2022 - 10h30 04/05/2022 - 14h sub-049 25/04/2022 - 16h 29/04/2022 - 9h sub-050 26/04/2022 - 13h 27/04/2022 - 10h sub-051 27/04/2022 - 9h 03/05/2022 - 14h30 sub-052 27/04/2022 - 16h 02/05/2022 - 17h sub-053 28/04/2022 - 13h 13/05/2022 - 14h sub-054 28/04/2022 - 16h 04/05/2022 - 16h sub-055 29/04/2022 - 13h 05/05/2022 - 16h sub-056 29/04/2022 - 17h 03/05/2022 - 17h15 sub-057 06/05/2022 - 13h 10/05/2022 - 14h sub-058 09/05/2022 - 14h 16/05/2022 - 16h sub-059 09/05/2022 - 16h 16/05/2022 - 9h30 sub-060 10/05/2022 - 9h 11/05/2022 - 15h30 sub-061 10/05/2022 - 16h 12/05/2022 - 14h sub-062 11/05/2022 - 16h 17/05/2022 - 16h sub-063 12/05/2022 - 13h 19/05/2022 - 15h sub-064 13/05/2022 - 13h 20/05/2022 - 16h sub-065 17/05/2022 - 9h 18/05/2022 - 16h sub-066 18/05/2022 - 9h 19/05/2022 - 9h sub-067 18/05/2022 - 11h 20/05/2022 - 9h sub-068 19/05/2022 - 13h 25/05/2022 - 10h sub-069 20/05/2022 - 9h 23/05/2022 - 9h00 sub-070 24/05/2022 - 9h 31/05/2022 - 9h00 sub-071 25/05/2022 - 9h 31/05/2022 - 16h sub-072 31/05/2022 - 13h 01/06/2022 - 13h sub-073 01/05/2022 - 9h30 10/06/2022 - 14h sub-074 08/06/2022 - 13h30 15/06/2022 - 15h30 sub-075 10/06/2022 - 13h30 15/06/2022 - 10h sub-076 13/06/2022 - 16h 16/06/2022 - 17h sub-077 13/06/2022 - 18h30 15/06/2022 - 18h30 sub-078 15/06/2022 - 13h30 20/06/2022 - 16h sub-079 15/06/2022 - 16h 21/06/2022 - 16h sub-080 16/06/2022 - 16h 22/06/2022 - 17h sub-081 17/06/2022 - 9h30 21/06/2022 - 9h sub-082 17/06/2022 - 13h30 24/06/2022 - 11h sub-083 20/06/2022 - 13h30 23/06/2022 - 9h sub-084 21/06/2022 - 10h30 23/06/2022 - 14h sub-085 21/06/2022 - 16h 27/06/2022 - 16h sub-086 22/06/2022 - 16h 29/06/2022 - 11h30 sub-087 23/06/2022 - 9h30 12/07/2022 - 10h sub-088 24/06/2022 - 10h 30/06/2022 - 10h sub-089 24/06/2022 - 16h 27/06/2022 - 18h sub-090 27/06/2022 - 9h30 28/06/2022 - 14h sub-091 27/06/2022 - 16h 04/07/2022 - 10h sub-092 29/06/2022 - 16h 04/07/2022 - 16h sub-093 01/07/2022 - 16h 05/07/2022 - 17h30 sub-094 04/07/2022 - 9h30 05/07/2022 - 12h sub-095 08/07/2022 - 16h 13/07/2022 - 17h sub-096 11/07/2022 - 17h 26/07/2022 - 17h sub-097 12/07/2022 - 16h 19/07/2022 - 9h30 sub-098 20/07/2022 - 16h 25/07/2022 - 14h sub-099 25/07/2022 - 16h 01/08/2022 - 16h sub-100 26/07/2022 - 10h 01/08/2022 - 10h sub-101 05/09/2022 - 13h30 07/09/2022 - 13h30 sub-102 07/09/2022 - 13h30 08/09/2022 - 14h sub-103 26/09/2022 - 13h 27/09/2022 - 9h sub-104 03/10/2022 - 9h30 04/10/2022 - 9h30 sub-105 07/10/2022 - 14h 11/10/2022 - 17h30 sub-106 07/10/2022 - 16h No session sub-107 17/10/2022 - 16h30 20/10/2022 - 16h45 sub-108 19/10/2022 - 16h 20/10/2022 - 10h sub-109 20/10/2022 - 13h30 21/10/2022 - 15h sub-110 21/10/2022 - 9h30 25/10/2022 - 9h30 sub-111 26/10/2022 - 18h30 03/11/2022 - 18h30 sub-112 27/10/2022 - 16h 07/11/2022 - 14h sub-113 28/10/2022 - 17h30 02/11/2022 - 9h sub-114 10/11/2022 - 13h30 10/11/2022 - 15h sub-115 24/11/2022 - 13h 01/12/2022 - 13h30 sub-116 29/11/2022 - 10h 08/12/2022 - 13h sub-117 02/12/2022 - 15h30 09/12/2022 - 16h30 sub-118 05/12/2022 - 13h 20/12/2022 - 14h30 sub-119 05/12/2022 - 10h 12/12/2022 - 18h30 sub-120 07/12/2022 - 15h30 09/12/2022 - 15h00 sub-121 20/12/2022 - 10h 21/12/2022 - 10h sub-122 18/01/2023 - 10h 25/01/2023 - 13h sub-123 18/01/2023 - 13h 19/01/2023 - 14h sub-124 19/01/2023 - 14h 23/01/2023 - 14h sub-125 19/01/2023 - 16h 23/01/2023 - 9h sub-126 20/01/2023 - 16h 01/02/2023 - 15h sub-127 30/01/2023 - 17h 06/02/2023 - 15h30 sub-128 31/01/2023 - 15h30 06/02/2023 - 9h sub-129 01/02/2023 - 17h 02/02/2023 - 10h sub-130 03/02/2023 - 15h30 08/02/2023 - 16h sub-131 08/02/2023 - 10h 09/02/2023 - 10h sub-132 08/02/2023 - 15h30 15/02/2023 - 16h30 sub-133 14/02/2023 - 17h 20/02/2023 - 10h sub-134 15/03/2023 - 15h30 22/03/2023 - 15h30 sub-135 16/03/2023 - 15h00 17/03/2023 - 8h30 sub-136 23/03/2023 - 15h30 27/03/2023 - 10h30 sub-137 28/03/2023 - 13h30 29/03/2023 - 17h sub-138 29/03/2023 - 17h 03/04/2023 - 17h sub-139 30/03/2023 - 9h 31/03/2023 - 9h sub-140 31/03/2023 - 15h30 05/04/2023 - 8h30 sub-141 05/04/2023 - 9h 12/04/2023 - 15h30 sub-142 12/04/2023 - 11h 19/04/2023 - 11h sub-143 13/04/2023 - 15h30 19/04/2023 - 9h sub-144 17/04/2023 - 10h 20/04/2023 - 13h30 sub-145 19/04/2023 - 13h30 20/04/2023 - 9h sub-146 21/04/2023 - 10h 21/04/2023 - 10h sub-147 21/04/2023 - 17h 04/05/2023 - 14h sub-148 24/04/2023 - 14h 26/04/2023 - 14h sub-149 25/04/2023 - 15h 26/04/2023 - 15h30 sub-150 26/04/2023 - 15h 04/05/2023 - 14h sub-151 27/04/2023 - 15h30 02/05/2023 - 14h sub-152 05/05/2023 - 8h 09/05/2023 - 17h sub-153 05/05/2023 - 14h 11/05/2023 - 16h sub-154 11/05/2023 - 17h 15/05/2023 - 17h sub-155 12/05/2023 - 14h 22/05/2023 - 16h30 sub-156 12/05/2023 - 17h30 17/05/2023 - 17h30 sub-157 23/05/2023 - 10h 26/05/2023 - 14h sub-158 05/06/2023 - 17h 07/06/2023 - 16h15 sub-159 06/06/2023 - 11h 07/06/2023 - 10h sub-160 29/06/2023 - 16h30 30/06/2023 - 17h sub-161 06/07/2023 - 15h 10/07/2023 - 15h sub-162 10/07/2023 - 9h30 11/07/2023 - 15h sub-163 11/07/2023 - 10h No session sub-164 12/07/2023 - 16h30 13/07/2023 - 17h sub-165 17/07/2023 - 16h30 18/07/2023 - 17h

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution (n=165, range 18–36 yr, mean 24.8 yr · sex per subject not reported)

1520253035

Sex composition

165
subjects
Other
165

Channel counts: 64 ch (n=165 recordings)

Sampling frequencies (Hz)

5122048

Total recording duration: 14 h 8 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 512 Hz · mixed · 165 subjects, 165 recordings
Live trace viewer — sub-021 · task-restingstate

Showing one representative recording out of 165 subjects and 165 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 · 64 sensors — 64 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 — DS005305
HED event descriptors word cloud HED event descriptors word cloud — DS005305
§ 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

DS005305

Title

EEG Resting-state Microstates Correlates of Executive Functions

Author (year)

Quentin2024

Canonical

—

Importable as

DS005305, Quentin2024

Year

20

Authors

Chenot Quentin, Hamery Caroline, Truninger Moritz, De Boissezon Xavier, Langer Nicolas, Scannella Sébastien

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005305.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005305,
  title = {EEG Resting-state Microstates Correlates of Executive Functions},
  author = {Chenot Quentin and Hamery Caroline and Truninger Moritz and De Boissezon Xavier and Langer Nicolas and Scannella Sébastien},
  doi = {10.18112/openneuro.ds005305.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005305.v1.0.1},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS005305(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Quentin2024
Canonical—
Importable asDS005305 · Quentin2024
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS005305(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

EEG Resting-state Microstates Correlates of Executive Functions

Study:

ds005305 (OpenNeuro)

Author (year):

Quentin2024

Canonical:

—

Also importable as: DS005305, Quentin2024.

Modality: eeg. Subjects: 165; recordings: 165; 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/ds005305 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005305 DOI: https://doi.org/10.18112/openneuro.ds005305.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005305
>>> dataset = DS005305(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/ds005305 · pull with datasets.load_dataset("EEGDash/ds005305").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS005305.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 ds005305 to reproduce the tutorial on this dataset.

Citation

Chenot Quentin, Hamery Caroline, Truninger Moritz, De Boissezon Xavier, Langer Nicolas, … (20). EEG Resting-state Microstates Correlates of Executive Functions. 10.18112/openneuro.ds005305.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds005305.v1.0.1.

BIDS
BIDS 1.8.0
Sidecars
events · channels · electrodes · coordsystem · eeg.json
Machine-readable
schema.org/Dataset · Croissant

See Also#