EEGdash›NeMAR›NM000319

Iss. 319 · 8 subjects · 49 recordings · CC-BY-4.0

Dataset Brief · Kueper2024

NM000319: eeg dataset, 8 subjects#

Kueper2024: Unilateral vs bilateral movement-execution EEG dataset

Access recordings and metadata through EEGDash.

Citation: Niklas Kueper, Su Kyoung Kim, Elsa Andrea Kirchner (2023). Kueper2024: Unilateral vs bilateral movement-execution EEG dataset. 10.82901/nemar.nm000319

Modality: eeg Subjects: 8 Recordings: 49 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

8-participant EEG dataset — Kueper2024: Unilateral vs bilateral movement-execution EEG dataset.

Data & curation Niklas Kueper · Su Kyoung Kim · Elsa Andrea Kirchner
Senior author Elsa Andrea Kirchner
Year 2023 · Distributed via NeMAR

EEG · 64 ch500 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 NM000319

dataset = NM000319(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 = NM000319(cache_dir="./data", subject="01")

Advanced query

dataset = NM000319(
    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{nm000319,
  title = {Kueper2024: Unilateral vs bilateral movement-execution EEG dataset},
  author = {Niklas Kueper and Su Kyoung Kim and Elsa Andrea Kirchner},
  doi = {10.82901/nemar.nm000319},
  url = {https://doi.org/10.82901/nemar.nm000319},
}

§ 02Study · The README

About This Dataset#

Unilateral vs bilateral movement-execution EEG dataset [1]_, [2]_.

Code: Kueper2024

Paradigm: imagery DOI: 10.1038/s41598-024-65910-8 Subjects: 8 Sessions per subject: 1 Events: unilateral=1, bilateral=2 Trial interval: (-2.0, 1.0) s Runs per session: 6 File format: BrainVision

DOI

Kueper2024

Acquisition

Sampling rate: 500.0 Hz Number of channels: 67 Channel types: eeg=64, misc=3 Channel names: Fp1, Fp2, F7, F3, Fz, F4, F8, FC5, FC1, FC2, FC6, T7, C3, Cz, C4, T8, TP9, CP5, CP1, CP2, CP6, TP10, P7, P3, Pz, P4, P8, PO9, O1, Oz, O2, PO10, AF7, AF3, AF4, AF8, F5, F1, F2, F6, FT9, FT7, FC3, FC4, FT8, FT10, C5, C1, C2, C6, TP7, CP3, CPz, CP4, TP8, P5, P1, P2, P6, PO7, PO3, POz, PO4, PO8

View full README

DOI

Kueper2024

Acquisition

Sampling rate: 500.0 Hz Number of channels: 67 Channel types: eeg=64, misc=3 Channel names: Fp1, Fp2, F7, F3, Fz, F4, F8, FC5, FC1, FC2, FC6, T7, C3, Cz, C4, T8, TP9, CP5, CP1, CP2, CP6, TP10, P7, P3, Pz, P4, P8, PO9, O1, Oz, O2, PO10, AF7, AF3, AF4, AF8, F5, F1, F2, F6, FT9, FT7, FC3, FC4, FT8, FT10, C5, C1, C2, C6, TP7, CP3, CPz, CP4, TP8, P5, P1, P2, P6, PO7, PO3, POz, PO4, PO8 Montage: extended 10-20 Hardware: Brain Products LiveAmp64 (wireless, active electrodes) Reference: FCz Ground: AFz Sensor type: active Line frequency: 50.0 Hz Cap manufacturer: Brain Products GmbH Cap model: Acticap slim Electrode type: active

Participants

Number of subjects: 8 Health status: healthy Age: mean=25.5, std=4.0 Gender distribution: male=4, female=4

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: unilateral, bilateral Trial duration: 3.0 s Study design: Self-initiated, self-paced reaching movements in two conditions: unilateral (right arm only, button press) and bilateral (both arms synchronously). 3 sets of 40 movements per condition. Feedback type: none Synchronicity: self-paced Mode: offline

HED Event Annotations

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

     ├─ Sensory-event
     └─ Label/unilateral

bilateral

├─ Sensory-event
└─ Label/bilateral

Data Structure

Trials: 240 Trials per class: unilateral=120, bilateral=120 Blocks per session: 6 Trials context: 3 sets of 40 self-initiated movements per task (unilateral, bilateral) by design; one run per set, 6 runs per subject (subject XP01 has an extra unilateral set in the release).

Preprocessing

Data state: raw Preprocessing applied: False Highpass filter: 0.1 Hz Lowpass filter: 131.0 Hz Notes: Hardware-prefiltered by the amplifier to 0.1-131 Hz; no further preprocessing applied.

Tags

Modality: Motor Type: Movement Execution

Documentation

Description: EEG dataset of self-initiated unilateral (right arm) and bilateral movement executions from 8 healthy subjects, for movement intention recognition in exoskeleton-supported rehabilitation. DOI: 10.1038/s41598-024-65910-8 License: CC-BY-4.0 Investigators: Niklas Kueper, Su Kyoung Kim, Elsa Andrea Kirchner Institution: German Research Centre for Artificial Intelligence (DFKI) Country: DE Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.10229480 Publication year: 2024 Keywords: EEG, movement intention, ERP, BCI, stroke rehabilitation, LRP

References

Kueper, N., Kim, S. K., & Kirchner, E. A. (2023). EEG Dataset of Unilateral and Bilateral Movement Executions [Data set]. Zenodo. DOI: https://doi.org/10.5281/zenodo.10229480 Kueper, N., Kim, S. K., & Kirchner, E. A. (2024). Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported rehabilitation through transfer learning on EEG data. Scientific Reports, 14(1), 16690. DOI: https://doi.org/10.1038/s41598-024-65910-8 Notes .. 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.nm000319-blue)](https://doi.org/10.82901/nemar.nm000319) Kueper2024 ========== Unilateral vs bilateral movement-execution EEG dataset [1]_, [2]_. Dataset Overview —————-

Code: Kueper2024 Paradigm: imagery DOI: 10.1038/s41598-024-65910-8 Subjects: 8 Sessions per subject: 1 Events: unilateral=1, bilateral=2 Trial interval: (-2.0, 1.0) s Runs per session: 6 File format: BrainVision

Acquisition#

Sampling rate: 500.0 Hz Number of channels: 67 Channel types: eeg=64, misc=3 Channel names: Fp1, Fp2, F7, F3, Fz, F4, F8, FC5, FC1, FC2, FC6, T7, C3, Cz, C4, T8, TP9, CP5, CP1, CP2, CP6, TP10, P7, P3, Pz, P4, P8, PO9, O1, Oz, O2, PO10, AF7, AF3, AF4, AF8, F5, F1, F2, F6, FT9, FT7, FC3, FC4, FT8, FT10, C5, C1, C2, C6, TP7, CP3, CPz, CP4, TP8, P5, P1, P2, P6, PO7, PO3, POz, PO4, PO8 Montage: extended 10-20 Hardware: Brain Products LiveAmp64 (wireless, active electrodes) Reference: FCz Ground: AFz Sensor type: active Line frequency: 50.0 Hz Cap manufacturer: Brain Products GmbH Cap model: Acticap slim Electrode type: active

Participants#

Number of subjects: 8 Health status: healthy Age: mean=25.5, std=4.0 Gender distribution: male=4, female=4

Experimental Protocol#

Paradigm: imagery Number of classes: 2 Class labels: unilateral, bilateral Trial duration: 3.0 s Study design: Self-initiated, self-paced reaching movements in two conditions: unilateral (right arm only, button press) and bilateral (both arms synchronously). 3 sets of 40 movements per condition. Feedback type: none Synchronicity: self-paced Mode: offline

HED Event Annotations#

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

├─ Sensory-event └─ Label/unilateral

bilateral
├─ Sensory-event └─ Label/bilateral

Data Structure#

Trials: 240 Trials per class: unilateral=120, bilateral=120 Blocks per session: 6 Trials context: 3 sets of 40 self-initiated movements per task (unilateral, bilateral) by design; one run per set, 6 runs per subject (subject XP01 has an extra unilateral set in the release).

Preprocessing#

Data state: raw Preprocessing applied: False Highpass filter: 0.1 Hz Lowpass filter: 131.0 Hz Notes: Hardware-prefiltered by the amplifier to 0.1-131 Hz; no further preprocessing applied.

Tags#

Modality: Motor Type: Movement Execution

Documentation#

Description: EEG dataset of self-initiated unilateral (right arm) and bilateral movement executions from 8 healthy subjects, for movement intention recognition in exoskeleton-supported rehabilitation. DOI: 10.1038/s41598-024-65910-8 License: CC-BY-4.0 Investigators: Niklas Kueper, Su Kyoung Kim, Elsa Andrea Kirchner Institution: German Research Centre for Artificial Intelligence (DFKI) Country: DE Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.10229480 Publication year: 2024 Keywords: EEG, movement intention, ERP, BCI, stroke rehabilitation, LRP

References#

Kueper, N., Kim, S. K., & Kirchner, E. A. (2023). EEG Dataset of Unilateral and Bilateral Movement Executions [Data set]. Zenodo. DOI: https://doi.org/10.5281/zenodo.10229480 Kueper, N., Kim, S. K., & Kirchner, E. A. (2024). Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported rehabilitation through transfer learning on EEG data. Scientific Reports, 14(1), 16690. DOI: https://doi.org/10.1038/s41598-024-65910-8 Notes .. 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:

Niklas Kueper
Su Kyoung Kim
Elsa Andrea Kirchner

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000319

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=8, range 26–26 yr, mean 25.0 yr)

25

Other · 8

Channel counts: 64 ch (n=49 recordings)

Sampling frequencies: 500.0 Hz (n=49 recordings)

Total recording duration: 6 h 55 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 500 Hz · 8 subjects, 49 recordings

Live trace viewer — sub-7 · ses-0 · task-imagery · run-3

Showing one representative recording out of 8 subjects and 49 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.

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.NM000319(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000319

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

class eegdash.dataset.NM000319(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Kueper2024: Unilateral vs bilateral movement-execution EEG dataset

Study:: nm000319 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000319.

Modality: eeg; Subject type: Unknown. Subjects: 8; recordings: 49; 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/nm000319 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000319 DOI: https://doi.org/10.82901/nemar.nm000319

Examples

>>> from eegdash.dataset import NM000319
>>> dataset = NM000319(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 — NM000319.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 nm000319 to reproduce the tutorial on this dataset.

Citation

Niklas Kueper, Su Kyoung Kim, Elsa Andrea Kirchner (2023). Kueper2024: Unilateral vs bilateral movement-execution EEG dataset. 10.82901/nemar.nm000319

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000319.

Related & sibling datasets

NM000306EEG · 15 subj NM000293EEG · 11 subj NM000307EEG · 2 subj NM000385EEG · 14 subj NM000150EEG

+ 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.nm000319

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000319`
Title	Kueper2024: Unilateral vs bilateral movement-execution EEG dataset
Author (year)	—
Canonical	—
Importable as	`NM000319`
Year	2023
Authors	Niklas Kueper, Su Kyoung Kim, Elsa Andrea Kirchner
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000319
Source links	OpenNeuro \| NeMAR \| Source URL

NM000319: eeg dataset, 8 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

Data Structure#

Preprocessing#

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#