EEGdash›NeMAR›NM000294

Iss. 294 · 12 subjects · 144 recordings · CC-BY-4.0

Dataset Brief · Farabbi2020

NM000294: eeg dataset, 12 subjects#

Farabbi2020: Motor-Imagery EEG dataset during robot-arm control

Access recordings and metadata through EEGDash.

Citation: Andrea Farabbi, Fabiola Ghiringhelli, Luca Mainardi, Joao Miguel Sanches, Plinio Moreno, Jose Santos-Victor, Patricia Figueiredo, Athanasios Vourvopoulos (2020). Farabbi2020: Motor-Imagery EEG dataset during robot-arm control. 10.82901/nemar.nm000294

Modality: eeg Subjects: 12 Recordings: 144 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

12-participant EEG dataset — Farabbi2020: Motor-Imagery EEG dataset during robot-arm control.

Data & curation Andrea Farabbi · Fabiola Ghiringhelli · Luca Mainardi · Joao Miguel Sanches · Plinio Moreno · Jose Santos-Victor · …
Senior author Athanasios Vourvopoulos
Year 2020 · Distributed via NeMAR

EEG · 32 ch250 HzBIDS 1.9.0Task · imagery3 sessions

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 NM000294

dataset = NM000294(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 = NM000294(cache_dir="./data", subject="01")

Advanced query

dataset = NM000294(
    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{nm000294,
  title = {Farabbi2020: Motor-Imagery EEG dataset during robot-arm control},
  author = {Andrea Farabbi and Fabiola Ghiringhelli and Luca Mainardi and Joao Miguel Sanches and Plinio Moreno and Jose Santos-Victor and Patricia Figueiredo and Athanasios Vourvopoulos},
  doi = {10.82901/nemar.nm000294},
  url = {https://doi.org/10.82901/nemar.nm000294},
}

§ 02Study · The README

About This Dataset#

Motor-Imagery EEG dataset during robot-arm control [1]_.

Code: Farabbi2020

Paradigm: imagery DOI: 10.5281/zenodo.5882500 Subjects: 12 Sessions per subject: 3 Events: left_hand=769, right_hand=770 Trial interval: [0, 4] s Runs per session: 4 File format: GDF

DOI

Farabbi2020

Acquisition

Sampling rate: 250.0 Hz Number of channels: 35 Channel types: eeg=32, misc=3 Channel names: Fp1, Fz, F3, F7, FT9, FC5, FC1, C3, T7, TP9, CP5, CP1, Pz, P3, P7, O1, Oz, O2, P4, P8, TP10, CP6, CP2, Cz, C4, T8, FT10, FC6, FC2, F4, F8, Fp2

View full README

DOI

Farabbi2020

Acquisition

Sampling rate: 250.0 Hz Number of channels: 35 Channel types: eeg=32, misc=3 Channel names: Fp1, Fz, F3, F7, FT9, FC5, FC1, C3, T7, TP9, CP5, CP1, Pz, P3, P7, O1, Oz, O2, P4, P8, TP10, CP6, CP2, Cz, C4, T8, FT10, FC6, FC2, F4, F8, Fp2 Montage: standard_1020 Hardware: LiveAmp 32 (Brain Products GmbH) Sensor type: active Ag/AgCl Line frequency: 50.0 Hz Cap manufacturer: Brain Products GmbH Cap model: actiCAP Electrode type: active

Participants

Number of subjects: 12 Health status: healthy BCI experience: naive

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 6.0 s Study design: Cue-based left- vs right-hand motor imagery controlling a Baxter robot arm reaching toward objects, under first-person and third-person visual feedback. Feedback type: visual Stimulus type: visual Stimulus modalities: visual Synchronicity: cue-based Mode: online Training/test split: True Instructions: Imagine left- or right-hand movement following the cue to steer the robot arm toward the target object.

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

Paradigm-Specific Parameters

Detected paradigm: motor_imagery Imagery tasks: left_hand, right_hand Imagery duration: 4.0 s

Data Structure

Trials: 480 Trials per class: left_hand=240, right_hand=240 Blocks per session: 4 Trials context: Three sessions per subject, each with four motor-imagery runs (first-person training/online, third-person training/online) plus an ignored resting-state recording. Each run: 20 left + 20 right trials (40); 160 per session, 480 per subject. Each trial: 2 s baseline + 4 s imagery.

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, cross_session

BCI Application

Applications: robot arm control, neurorehabilitation Environment: lab Online feedback: True

Tags

Pathology: healthy Modality: motor Type: Motor Imagery

Documentation

Description: Motor-imagery EEG from 12 healthy naive subjects performing cued left/right-hand imagery to control a Baxter robot arm, across three sessions with first- and third-person visual feedback conditions. DOI: 10.5281/zenodo.5882500 License: CC-BY-4.0 Investigators: Andrea Farabbi, Fabiola Ghiringhelli, Luca Mainardi, Joao Miguel Sanches, Plinio Moreno, Jose Santos-Victor, Patricia Figueiredo, Athanasios Vourvopoulos Institution: Politecnico di Milano; Instituto Superior Tecnico, Universidade de Lisboa Country: IT Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.5882500 Publication year: 2020 Keywords: motor imagery, BCI, brain-computer interface, EEG, robot arm, neurorehabilitation

References

Farabbi, A., Ghiringhelli, F., Mainardi, L., Sanches, J. M., Moreno, P., Santos-Victor, J., Figueiredo, P., and Vourvopoulos, A. (2020). Motor-Imagery EEG Dataset During Robot-Arm Control. Zenodo. DOI: https://doi.org/10.5281/zenodo.5882500 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.

Ethics

Approved by the Ethics Committee of CHULN and CAML (Faculty of Medicine, University of Lisbon), reference number 245/19. Written informed consent was obtained from all participants in accordance with the 1964 Declaration of Helsinki (Farabbi et al. 2020, Zenodo DOI 10.5281/zenodo.5882500).

Verbatim from the source:

Approved by the Ethics Committee of CHULN and CAML (Faculty of Medicine, University of Lisbon) with reference number: 245/19.

Source: Zenodo record 5882500 ‘notes’ field (https://zenodo.org/records/5882500).

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000294-blue)](https://doi.org/10.82901/nemar.nm000294) Farabbi2020 =========== Motor-Imagery EEG dataset during robot-arm control [1]_. Dataset Overview —————-

Code: Farabbi2020 Paradigm: imagery DOI: 10.5281/zenodo.5882500 Subjects: 12 Sessions per subject: 3 Events: left_hand=769, right_hand=770 Trial interval: [0, 4] s Runs per session: 4 File format: GDF

Acquisition#

Sampling rate: 250.0 Hz Number of channels: 35 Channel types: eeg=32, misc=3 Channel names: Fp1, Fz, F3, F7, FT9, FC5, FC1, C3, T7, TP9, CP5, CP1, Pz, P3, P7, O1, Oz, O2, P4, P8, TP10, CP6, CP2, Cz, C4, T8, FT10, FC6, FC2, F4, F8, Fp2 Montage: standard_1020 Hardware: LiveAmp 32 (Brain Products GmbH) Sensor type: active Ag/AgCl Line frequency: 50.0 Hz Cap manufacturer: Brain Products GmbH Cap model: actiCAP Electrode type: active

Participants#

Number of subjects: 12 Health status: healthy BCI experience: naive

Experimental Protocol#

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 6.0 s Study design: Cue-based left- vs right-hand motor imagery controlling a Baxter robot arm reaching toward objects, under first-person and third-person visual feedback. Feedback type: visual Stimulus type: visual Stimulus modalities: visual Synchronicity: cue-based Mode: online Training/test split: True Instructions: Imagine left- or right-hand movement following the cue to steer the robot arm toward the target object.

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

Paradigm-Specific Parameters#

Detected paradigm: motor_imagery Imagery tasks: left_hand, right_hand Imagery duration: 4.0 s

Data Structure#

Trials: 480 Trials per class: left_hand=240, right_hand=240 Blocks per session: 4 Trials context: Three sessions per subject, each with four motor-imagery runs (first-person training/online, third-person training/online) plus an ignored resting-state recording. Each run: 20 left + 20 right trials (40); 160 per session, 480 per subject. Each trial: 2 s baseline + 4 s imagery.

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, cross_session

BCI Application#

Applications: robot arm control, neurorehabilitation Environment: lab Online feedback: True

Tags#

Pathology: healthy Modality: motor Type: Motor Imagery

Documentation#

Description: Motor-imagery EEG from 12 healthy naive subjects performing cued left/right-hand imagery to control a Baxter robot arm, across three sessions with first- and third-person visual feedback conditions. DOI: 10.5281/zenodo.5882500 License: CC-BY-4.0 Investigators: Andrea Farabbi, Fabiola Ghiringhelli, Luca Mainardi, Joao Miguel Sanches, Plinio Moreno, Jose Santos-Victor, Patricia Figueiredo, Athanasios Vourvopoulos Institution: Politecnico di Milano; Instituto Superior Tecnico, Universidade de Lisboa Country: IT Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.5882500 Publication year: 2020 Keywords: motor imagery, BCI, brain-computer interface, EEG, robot arm, neurorehabilitation

References#

Farabbi, A., Ghiringhelli, F., Mainardi, L., Sanches, J. M., Moreno, P., Santos-Victor, J., Figueiredo, P., and Vourvopoulos, A. (2020). Motor-Imagery EEG Dataset During Robot-Arm Control. Zenodo. DOI: https://doi.org/10.5281/zenodo.5882500 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. ## Ethics Approved by the Ethics Committee of CHULN and CAML (Faculty of Medicine, University of Lisbon), reference number 245/19. Written informed consent was obtained from all participants in accordance with the 1964 Declaration of Helsinki (Farabbi et al. 2020, Zenodo DOI 10.5281/zenodo.5882500). Verbatim from the source: > Approved by the Ethics Committee of CHULN and CAML (Faculty of Medicine, University of Lisbon) with reference number: 245/19. Source: Zenodo record 5882500 ‘notes’ field (https://zenodo.org/records/5882500).

License: CC-BY-4.0

Authors:

Andrea Farabbi
Fabiola Ghiringhelli
Luca Mainardi
Joao Miguel Sanches
Plinio Moreno
… and 3 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000294

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 32 ch (n=144 recordings)

Sampling frequencies: 250.0 Hz (n=144 recordings)

Total recording duration: 17 h 32 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 ch · EEG · 250 Hz · 12 subjects, 144 recordings

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

Showing one representative recording out of 12 subjects and 144 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 · 32 sensors — 32 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.NM000294(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000294

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

class eegdash.dataset.NM000294(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Farabbi2020: Motor-Imagery EEG dataset during robot-arm control

Study:: nm000294 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000294.

Modality: eeg; Subject type: Unknown. Subjects: 12; recordings: 144; 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/nm000294 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000294 DOI: https://doi.org/10.82901/nemar.nm000294

Examples

>>> from eegdash.dataset import NM000294
>>> dataset = NM000294(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 — NM000294.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 nm000294 to reproduce the tutorial on this dataset.

Citation

Andrea Farabbi, Fabiola Ghiringhelli, Luca Mainardi, Joao Miguel Sanches, Plinio Moreno, … (2020). Farabbi2020: Motor-Imagery EEG dataset during robot-arm control. 10.82901/nemar.nm000294

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000294.

Related & sibling datasets

NM000315EEG · 10 subj NM000302EEG · 6 subj NM000320EEG · 10 subj NM000291EEG · 14 subj NM000300EEG · 10 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.nm000294

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000294`
Title	Farabbi2020: Motor-Imagery EEG dataset during robot-arm control
Author (year)	—
Canonical	—
Importable as	`NM000294`
Year	2020
Authors	Andrea Farabbi, Fabiola Ghiringhelli, Luca Mainardi, Joao Miguel Sanches, Plinio Moreno, Jose Santos-Victor, Patricia Figueiredo, Athanasios Vourvopoulos
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000294
Source links	OpenNeuro \| NeMAR \| Source URL

NM000294: eeg dataset, 12 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#