EEGdash›NeMAR›NM000307

Iss. 307 · 2 subjects · 32 recordings · CC-BY-4.0

Dataset Brief · Perdikis2018

NM000307: eeg dataset, 2 subjects#

Perdikis2018: CNBI EPFL Cybathlon BCI-race motor-imagery dataset

Access recordings and metadata through EEGDash.

Citation: Serafeim Perdikis, Luca Tonin, Sareh Saeedi, Christoph Schneider, Jose del R. Millan (2018). Perdikis2018: CNBI EPFL Cybathlon BCI-race motor-imagery dataset. 10.82901/nemar.nm000307

Modality: eeg Subjects: 2 Recordings: 32 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

2-participant EEG dataset — Perdikis2018: CNBI EPFL Cybathlon BCI-race motor-imagery dataset.

Data & curation Serafeim Perdikis · Luca Tonin · Sareh Saeedi · Christoph Schneider · Jose del R. Millan
Senior author Jose del R. Millan
Year 2018 · Distributed via NeMAR

EEG · 16 ch512 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 NM000307

dataset = NM000307(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 = NM000307(cache_dir="./data", subject="01")

Advanced query

dataset = NM000307(
    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{nm000307,
  title = {Perdikis2018: CNBI EPFL Cybathlon BCI-race motor-imagery dataset},
  author = {Serafeim Perdikis and Luca Tonin and Sareh Saeedi and Christoph Schneider and Jose del R. Millan},
  doi = {10.82901/nemar.nm000307},
  url = {https://doi.org/10.82901/nemar.nm000307},
}

§ 02Study · The README

About This Dataset#

CNBI EPFL Cybathlon BCI-race motor-imagery dataset [1]_.

Code: Perdikis2018

Paradigm: imagery DOI: 10.5281/zenodo.841764 Subjects: 2 Sessions per subject: 1 Events: both_feet=771, both_hands=773, rest=783 Trial interval: [1, 5] s File format: GDF

DOI

Perdikis2018

Acquisition

Sampling rate: 512.0 Hz Number of channels: 16 Channel types: eeg=16 Channel names: Fz, FC3, FC1, FCz, FC2, FC4, C3, C1, Cz, C2, C4, CP3, CP1, CPz, CP2, CP4

View full README

DOI

Perdikis2018

Acquisition

Sampling rate: 512.0 Hz Number of channels: 16 Channel types: eeg=16 Channel names: Fz, FC3, FC1, FCz, FC2, FC4, C3, C1, Cz, C2, C4, CP3, CP1, CPz, CP2, CP4 Montage: standard_1005 Hardware: g.USBamp (g.tec medical engineering, Austria) Sensor type: Ag/AgCl Line frequency: 50.0 Hz

Participants

Number of subjects: 2 Health status: spinal cord injury Clinical population: tetraplegia (chronic spinal cord injury) BCI experience: experienced

Experimental Protocol

Paradigm: imagery Number of classes: 3 Class labels: both_feet, both_hands, rest Trial duration: 4.0 s Study design: Longitudinal motor-imagery BCI training for the Cybathlon 2016 BCI race; two tetraplegic pilots delivered sustained kinesthetic motor-imagery commands (both hands, both feet, rest) to drive an avatar in the BrainRunners game. Feedback type: visual Stimulus type: visual Stimulus modalities: visual Synchronicity: cue-based Mode: offline Training/test split: False Instructions: Perform the cued kinesthetic motor imagery (both-hands, both-feet, or rest) to control the BrainRunners avatar.

HED Event Annotations

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

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Rest

both_feet

     ├─ Sensory-event
     └─ Label/both_feet

both_hands

├─ Sensory-event
└─ Label/both_hands

Paradigm-Specific Parameters

Detected paradigm: motor_imagery Imagery tasks: both_hands, both_feet, rest Cue duration: 1.0 s Imagery duration: 4.0 s

Data Structure

Trials context: Offline calibration runs of the both-hands / both-feet / rest family, each with about 15 cued trials per class present; runs span several recording days per pilot and are pooled as runs of a single session.

Preprocessing

Data state: raw Preprocessing applied: False

Signal Processing

Frequency bands: mu=[8.0, 12.0] Hz; beta=[12.0, 30.0] Hz

Cross-Validation

Evaluation type: within_subject

BCI Application

Applications: avatar control, BCI game (BrainRunners), Cybathlon Environment: lab and competition arena Online feedback: True

Tags

Pathology: spinal cord injury Modality: motor Type: Motor Imagery

Documentation

Description: EEG recordings and application logs from the two tetraplegic pilots of team Brain Tweakers (CNBI, EPFL) during longitudinal motor-imagery BCI training and the Cybathlon 2016 BCI race; this loader exposes the cue-based offline calibration runs (both-hands / both-feet / rest). DOI: 10.5281/zenodo.841764 Associated paper DOI: 10.1371/journal.pbio.2003787 License: CC-BY-4.0 Investigators: Serafeim Perdikis, Luca Tonin, Sareh Saeedi, Christoph Schneider, Jose del R. Millan Senior author: Jose del R. Millan Institution: Defitech Chair in Brain-Machine Interface (CNBI), Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne (EPFL) Country: CH Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.841764 Publication year: 2018 Keywords: motor imagery, brain-computer interface, EEG, Cybathlon, BCI race, spinal cord injury, tetraplegia

References

Perdikis, S., Tonin, L., Saeedi, S., Schneider, C., & Millan, J. del R. (2018). The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users. PLoS Biology, 16(5), e2003787. DOI: https://doi.org/10.1371/journal.pbio.2003787 Data: https://doi.org/10.5281/zenodo.841764 Notes .. versionadded:: 1.8 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.

Ethics

Approved by the Swiss Cantonal Committee of Vaud for ethics in human research (CER-VD), protocol number PB_2017–00295 (20/15 CCVEM). Informed consents were signed in accordance with the Declaration of Helsinki (Perdikis et al. 2018, PLoS Biology, DOI 10.1371/journal.pbio.2003787).

Verbatim from the source:

This study has been approved by the Cantonal Committee of Vaud (VD, Switzerland) for ethics in human research (CER-VD) under protocol number PB_2017–00295 (20/15 CCVEM).

Source: cached paper .paper-audit/Perdikis2018/paper-10_1371_journal_pbio_2003787.txt (Perdikis et al. 2018, PLoS Biol., DOI 10.1371/journal.pbio.2003787).

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000307-blue)](https://doi.org/10.82901/nemar.nm000307) Perdikis2018 ============ CNBI EPFL Cybathlon BCI-race motor-imagery dataset [1]_. Dataset Overview —————-

Code: Perdikis2018 Paradigm: imagery DOI: 10.5281/zenodo.841764 Subjects: 2 Sessions per subject: 1 Events: both_feet=771, both_hands=773, rest=783 Trial interval: [1, 5] s File format: GDF

Acquisition#

Sampling rate: 512.0 Hz Number of channels: 16 Channel types: eeg=16 Channel names: Fz, FC3, FC1, FCz, FC2, FC4, C3, C1, Cz, C2, C4, CP3, CP1, CPz, CP2, CP4 Montage: standard_1005 Hardware: g.USBamp (g.tec medical engineering, Austria) Sensor type: Ag/AgCl Line frequency: 50.0 Hz

Participants#

Number of subjects: 2 Health status: spinal cord injury Clinical population: tetraplegia (chronic spinal cord injury) BCI experience: experienced

Experimental Protocol#

Paradigm: imagery Number of classes: 3 Class labels: both_feet, both_hands, rest Trial duration: 4.0 s Study design: Longitudinal motor-imagery BCI training for the Cybathlon 2016 BCI race; two tetraplegic pilots delivered sustained kinesthetic motor-imagery commands (both hands, both feet, rest) to drive an avatar in the BrainRunners game. Feedback type: visual Stimulus type: visual Stimulus modalities: visual Synchronicity: cue-based Mode: offline Training/test split: False Instructions: Perform the cued kinesthetic motor imagery (both-hands, both-feet, or rest) to control the BrainRunners avatar.

HED Event Annotations#

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

├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Rest

both_feet
├─ Sensory-event └─ Label/both_feet

both_hands
├─ Sensory-event └─ Label/both_hands

Paradigm-Specific Parameters#

Detected paradigm: motor_imagery Imagery tasks: both_hands, both_feet, rest Cue duration: 1.0 s Imagery duration: 4.0 s

Data Structure#

Trials context: Offline calibration runs of the both-hands / both-feet / rest family, each with about 15 cued trials per class present; runs span several recording days per pilot and are pooled as runs of a single session.

Preprocessing#

Data state: raw Preprocessing applied: False

Signal Processing#

Frequency bands: mu=[8.0, 12.0] Hz; beta=[12.0, 30.0] Hz

Cross-Validation#

Evaluation type: within_subject

BCI Application#

Applications: avatar control, BCI game (BrainRunners), Cybathlon Environment: lab and competition arena Online feedback: True

Tags#

Pathology: spinal cord injury Modality: motor Type: Motor Imagery

Documentation#

Description: EEG recordings and application logs from the two tetraplegic pilots of team Brain Tweakers (CNBI, EPFL) during longitudinal motor-imagery BCI training and the Cybathlon 2016 BCI race; this loader exposes the cue-based offline calibration runs (both-hands / both-feet / rest). DOI: 10.5281/zenodo.841764 Associated paper DOI: 10.1371/journal.pbio.2003787 License: CC-BY-4.0 Investigators: Serafeim Perdikis, Luca Tonin, Sareh Saeedi, Christoph Schneider, Jose del R. Millan Senior author: Jose del R. Millan Institution: Defitech Chair in Brain-Machine Interface (CNBI), Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne (EPFL) Country: CH Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.841764 Publication year: 2018 Keywords: motor imagery, brain-computer interface, EEG, Cybathlon, BCI race, spinal cord injury, tetraplegia

References#

Perdikis, S., Tonin, L., Saeedi, S., Schneider, C., & Millan, J. del R. (2018). The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users. PLoS Biology, 16(5), e2003787. DOI: https://doi.org/10.1371/journal.pbio.2003787 Data: https://doi.org/10.5281/zenodo.841764 Notes .. versionadded:: 1.8 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. ## Ethics Approved by the Swiss Cantonal Committee of Vaud for ethics in human research (CER-VD), protocol number PB_2017–00295 (20/15 CCVEM). Informed consents were signed in accordance with the Declaration of Helsinki (Perdikis et al. 2018, PLoS Biology, DOI 10.1371/journal.pbio.2003787). Verbatim from the source: > This study has been approved by the Cantonal Committee of Vaud (VD, Switzerland) for ethics in human research (CER-VD) under protocol number PB_2017–00295 (20/15 CCVEM). Source: cached paper .paper-audit/Perdikis2018/paper-10_1371_journal_pbio_2003787.txt (Perdikis et al. 2018, PLoS Biol., DOI 10.1371/journal.pbio.2003787).

License: CC-BY-4.0

Authors:

Serafeim Perdikis
Luca Tonin
Sareh Saeedi
Christoph Schneider
Jose del R. Millan

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000307

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=2, range 2015–2015 yr)

2015

Other · 2

Channel counts: 16 ch (n=32 recordings)

Sampling frequencies: 512.0 Hz (n=32 recordings)

Total recording duration: 4 h 17 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 16 ch · EEG · 512 Hz · 2 subjects, 32 recordings

Live trace viewer — sub-1 · ses-0 · task-imagery · run-7

Showing one representative recording out of 2 subjects and 32 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 · 16 sensors — 16 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.NM000307(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000307

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

class eegdash.dataset.NM000307(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Perdikis2018: CNBI EPFL Cybathlon BCI-race motor-imagery dataset

Study:: nm000307 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000307.

Modality: eeg; Subject type: Unknown. Subjects: 2; recordings: 32; 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/nm000307 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000307 DOI: https://doi.org/10.82901/nemar.nm000307

Examples

>>> from eegdash.dataset import NM000307
>>> dataset = NM000307(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 — NM000307.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 nm000307 to reproduce the tutorial on this dataset.

Citation

Serafeim Perdikis, Luca Tonin, Sareh Saeedi, Christoph Schneider, Jose del R. Millan (2018). Perdikis2018: CNBI EPFL Cybathlon BCI-race motor-imagery dataset. 10.82901/nemar.nm000307

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000307.

Related & sibling datasets

NM000150EEG NM000293EEG · 11 subj NM000319EEG · 8 subj NM000308EEG · 2 subj NM000306EEG · 15 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.nm000307

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000307`
Title	Perdikis2018: CNBI EPFL Cybathlon BCI-race motor-imagery dataset
Author (year)	—
Canonical	—
Importable as	`NM000307`
Year	2018
Authors	Serafeim Perdikis, Luca Tonin, Sareh Saeedi, Christoph Schneider, Jose del R. Millan
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000307
Source links	OpenNeuro \| NeMAR \| Source URL

NM000307: eeg dataset, 2 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

Paradigm-Specific Parameters#

Data Structure#

Preprocessing#

Signal Processing#

Cross-Validation#

BCI Application#

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#