EEGdash›NeMAR›NM000295

Iss. 295 · 29 subjects · 87 recordings · CC-BY-4.0

Dataset Brief · Kodera2023

NM000295: eeg dataset, 29 subjects#

Kodera2023: Left/right-hand motor-imagery EEG dataset

Access recordings and metadata through EEGDash.

Citation: Jakub Kodera, Roman Moucek, Pavel Moutner, Pavel Mochura, Josef Yassin Saleh, Petr Bruha, Jana Solcova, Lukas Vareka, Pavel Snejdar (2023). Kodera2023: Left/right-hand motor-imagery EEG dataset. 10.82901/nemar.nm000295

Modality: eeg Subjects: 29 Recordings: 87 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

29-participant EEG dataset — Kodera2023: Left/right-hand motor-imagery EEG dataset.

Data & curation Jakub Kodera · Roman Moucek · Pavel Moutner · Pavel Mochura · Josef Yassin Saleh · Petr Bruha · …
Senior author Pavel Snejdar
Year 2023 · Distributed via NeMAR

EEG · 9 ch500, 1000 HzBIDS 1.9.0Task · imagery

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 NM000295

dataset = NM000295(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 = NM000295(cache_dir="./data", subject="01")

Advanced query

dataset = NM000295(
    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{nm000295,
  title = {Kodera2023: Left/right-hand motor-imagery EEG dataset},
  author = {Jakub Kodera and Roman Moucek and Pavel Moutner and Pavel Mochura and Josef Yassin Saleh and Petr Bruha and Jana Solcova and Lukas Vareka and Pavel Snejdar},
  doi = {10.82901/nemar.nm000295},
  url = {https://doi.org/10.82901/nemar.nm000295},
}

§ 02Study · The README

About This Dataset#

Left/right-hand motor-imagery EEG dataset [1]_.

Code: Kodera2023

Paradigm: imagery DOI: 10.5281/zenodo.7893846 Subjects: 29 Sessions per subject: 1 Events: left_hand=1, right_hand=2 Trial interval: [0, 4] s Runs per session: 4 File format: BrainVision

DOI

Kodera2023

Acquisition

Sampling rate: 500.0 Hz Number of channels: 9 Channel types: eeg=9 Channel names: Fz, Cz, Pz, F3, F4, P3, P4, C3, C4

View full README

DOI

Kodera2023

Acquisition

Sampling rate: 500.0 Hz Number of channels: 9 Channel types: eeg=9 Channel names: Fz, Cz, Pz, F3, F4, P3, P4, C3, C4 Montage: standard_1020 Hardware: BrainVision Recorder (BrainProducts) Line frequency: 50.0 Hz

Participants

Number of subjects: 29 Health status: healthy

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 4.0 s Study design: Cue-based single-class left- or right-hand motor imagery; each recording is one class, with the imagery cue onset marked by the S 1 BrainVision stimulus marker. Stimulus modalities: visual Mode: offline

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser left_hand

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Move
           └─ Left, Hand

right_hand

├─ Sensory-event, Experimental-stimulus, Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Right, Hand

Tags

Pathology: healthy Modality: motor Type: Motor Imagery

Documentation

Description: Left/right-hand motor-imagery EEG (University of West Bohemia): 29 subjects across two cohorts (16-channel 500 Hz and 9-channel 1000 Hz), 2 classes, BrainVision format. DOI: 10.5281/zenodo.7893846 License: CC-BY-4.0 Investigators: Jakub Kodera, Roman Moucek, Pavel Moutner, Pavel Mochura, Josef Yassin Saleh, Petr Bruha, Jana Solcova, Lukas Vareka, Pavel Snejdar Institution: University of West Bohemia Country: CZ Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.7893846 Publication year: 2023 Keywords: motor imagery, EEG, BCI, brain-computer interface, left hand, right hand

References

Kodera, J., Moucek, R., Moutner, P., Mochura, P., Saleh, J. Y., Bruha, P., Solcova, J., Vareka, L., and Snejdar, P. (2023). EEG motor imagery [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7893846 .. versionadded:: 1.8.0 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 Generated by MOABB 1.8.0dev0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

Ethics

Ethics approval: the data analysed in this deposit were collected under the ethics approval obtained by the original investigators and reported in the primary publication cited above (see References/Documentation sections of this README). Participants gave informed consent in the source study. No new human-subject data were collected during this BIDS re-release; this NEMAR record only reformats the published source data into BIDS via MOABB.

Please consult the primary publication for the exact IRB/ethics committee reference.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000295-blue)](https://doi.org/10.82901/nemar.nm000295) Kodera2023 ========== Left/right-hand motor-imagery EEG dataset [1]_. Dataset Overview —————-

Code: Kodera2023 Paradigm: imagery DOI: 10.5281/zenodo.7893846 Subjects: 29 Sessions per subject: 1 Events: left_hand=1, right_hand=2 Trial interval: [0, 4] s Runs per session: 4 File format: BrainVision

Acquisition#

Sampling rate: 500.0 Hz Number of channels: 9 Channel types: eeg=9 Channel names: Fz, Cz, Pz, F3, F4, P3, P4, C3, C4 Montage: standard_1020 Hardware: BrainVision Recorder (BrainProducts) Line frequency: 50.0 Hz

Participants#

Number of subjects: 29 Health status: healthy

Experimental Protocol#

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 4.0 s Study design: Cue-based single-class left- or right-hand motor imagery; each recording is one class, with the imagery cue onset marked by the S 1 BrainVision stimulus marker. Stimulus modalities: visual Mode: offline

HED Event Annotations#

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser left_hand

├─ Sensory-event, Experimental-stimulus, Visual-presentation └─ Agent-action

└─ Imagine
├─ Move └─ Left, Hand

right_hand
├─ Sensory-event, Experimental-stimulus, Visual-presentation └─ Agent-action

└─ Imagine
├─ Move └─ Right, Hand

Tags#

Pathology: healthy Modality: motor Type: Motor Imagery

Documentation#

Description: Left/right-hand motor-imagery EEG (University of West Bohemia): 29 subjects across two cohorts (16-channel 500 Hz and 9-channel 1000 Hz), 2 classes, BrainVision format. DOI: 10.5281/zenodo.7893846 License: CC-BY-4.0 Investigators: Jakub Kodera, Roman Moucek, Pavel Moutner, Pavel Mochura, Josef Yassin Saleh, Petr Bruha, Jana Solcova, Lukas Vareka, Pavel Snejdar Institution: University of West Bohemia Country: CZ Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.7893846 Publication year: 2023 Keywords: motor imagery, EEG, BCI, brain-computer interface, left hand, right hand

References#

Kodera, J., Moucek, R., Moutner, P., Mochura, P., Saleh, J. Y., Bruha, P., Solcova, J., Vareka, L., and Snejdar, P. (2023). EEG motor imagery [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7893846 .. versionadded:: 1.8.0 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 — Generated by MOABB 1.8.0dev0 (Mother of All BCI Benchmarks) NeuroTechX/moabb Ethics —— Ethics approval: the data analysed in this deposit were collected under the ethics approval obtained by the original investigators and reported in the primary publication cited above (see References/Documentation sections of this README). Participants gave informed consent in the source study. No new human-subject data were collected during this BIDS re-release; this NEMAR record only reformats the published source data into BIDS via MOABB. Please consult the primary publication for the exact IRB/ethics committee reference.

License: CC-BY-4.0

Authors:

Jakub Kodera
Roman Moucek
Pavel Moutner
Pavel Mochura
Josef Yassin Saleh
… and 4 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000295

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 9 ch (n=87 recordings)

Sampling frequencies (Hz)

5001000

Total recording duration: 17 h 44 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 9 ch · EEG · 500, 1000 Hz · 29 subjects, 87 recordings

Live trace viewer — sub-1 · ses-0 · task-imagery · run-0

Showing one representative recording out of 29 subjects and 87 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 · 9 sensors — 9 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.NM000295(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000295

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

class eegdash.dataset.NM000295(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Kodera2023: Left/right-hand motor-imagery EEG dataset

Study:: nm000295 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000295.

Modality: eeg; Subject type: Unknown. Subjects: 29; recordings: 87; 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/nm000295 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000295 DOI: https://doi.org/10.82901/nemar.nm000295

Examples

>>> from eegdash.dataset import NM000295
>>> dataset = NM000295(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 — NM000295.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 nm000295 to reproduce the tutorial on this dataset.

Citation

Jakub Kodera, Roman Moucek, Pavel Moutner, Pavel Mochura, Josef Yassin Saleh, … (2023). Kodera2023: Left/right-hand motor-imagery EEG dataset. 10.82901/nemar.nm000295

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000295.

Related & sibling datasets

NM000309EEG · 18 subj NM000330EEG · 22 subj NM000312EEG · 27 subj NM000316EEG · 5 subj NM000318EEG · 20 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000295

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000295`
Title	Kodera2023: Left/right-hand motor-imagery EEG dataset
Author (year)	—
Canonical	—
Importable as	`NM000295`
Year	2023
Authors	Jakub Kodera, Roman Moucek, Pavel Moutner, Pavel Mochura, Josef Yassin Saleh, Petr Bruha, Jana Solcova, Lukas Vareka, Pavel Snejdar
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000295
Source links	OpenNeuro \| NeMAR \| Source URL

NM000295: eeg dataset, 29 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

Tags#

Documentation#

References#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#