EEGdash›NeMAR›NM000322

Iss. 322 · 48 subjects · 234 recordings · CC-BY-4.0

Dataset Brief · NeBULA2025

NM000322: eeg dataset, 48 subjects#

NeBULA2025: Standardized reaching motor-execution EEG dataset (NeBULA)

Access recordings and metadata through EEGDash.

Citation: Federica Garro, Elisa Fenoglio, Ilaria Ceroni, Iryna Forsiuk, Michela Canepa, Marta Mozzon, Alessandro Bruschi, Fabio Zippo, Matteo Laffranchi, Lorenzo De Michieli, Stefano Buccelli, Michela Chiappalone, Marianna Semprini (2025). NeBULA2025: Standardized reaching motor-execution EEG dataset (NeBULA). 10.82901/nemar.nm000322

Modality: eeg Subjects: 48 Recordings: 234 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

48-participant EEG dataset — NeBULA2025: Standardized reaching motor-execution EEG dataset (NeBULA).

Data & curation Federica Garro · Elisa Fenoglio · Ilaria Ceroni · Iryna Forsiuk · Michela Canepa · Marta Mozzon · …
Senior author Marianna Semprini
Year 2025 · Distributed via NeMAR
Funding Istituto Nazionale Assicurazione Infortuni sul Lavoro (INAIL), project grant PR19-RR-P2

EEG · 127 ch1000 HzBIDS 1.9.04 tasks

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 NM000322

dataset = NM000322(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 = NM000322(cache_dir="./data", subject="01")

Advanced query

dataset = NM000322(
    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{nm000322,
  title = {NeBULA2025: Standardized reaching motor-execution EEG dataset (NeBULA)},
  author = {Federica Garro and Elisa Fenoglio and Ilaria Ceroni and Iryna Forsiuk and Michela Canepa and Marta Mozzon and Alessandro Bruschi and Fabio Zippo and Matteo Laffranchi and Lorenzo De Michieli and Stefano Buccelli and Michela Chiappalone and Marianna Semprini},
  doi = {10.82901/nemar.nm000322},
  url = {https://doi.org/10.82901/nemar.nm000322},
}

§ 02Study · The README

About This Dataset#

Standardized reaching motor-execution EEG dataset (NeBULA) [1]_.

Code: NeBULA2025

Paradigm: imagery DOI: 10.1038/s41597-025-05042-4 Subjects: 39 Sessions per subject: 1 Events: reach_1=1, reach_2=2, reach_3=3 Trial interval: [0, 2] s Runs per session: 3 File format: BrainVision (BIDS)

DOI

NeBULA2025

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 127 Channel types: eeg=127 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, AF7, AF3, AFz, F1, F5, FT7, FC3, C1, C5, TP7, CP3, P1, P5, PO7, PO3, POz, PO4, PO8, P6, P2, CPz, CP4, TP8, C6, C2, FC4, FT8, F6, AF8, AF4, F2, F9, AFF1h, FFC1h, FFC5h, FTT7h, FCC3h, CCP1h, CCP5h, TPP7h, P9, PPO9h, PO9, O9, OI1h, PPO1h, CPP3h, CPP4h, PPO2h, OI2h, O10, PO10, PPO10h, P10, TPP8h, CCP6h, CCP2h, FCC4h, FTT8h, FFC6h, FFC2h, AFF2h, F10, AFp1, AFF5h, FFT9h, FFT7h, FFC3h, FCC1h, FCC5h, FTT9h, TTP7h, CCP3h, CPP1h, CPP5h, TPP9h, POO9h, PPO5h, POO1, POO2, PPO6h, POO10h, TPP10h, CPP6h, CPP2h, CCP4h, TTP8h, FTT10h, FCC6h, FCC2h, FFC4h, FFT8h, FFT10h, AFF6h, AFp2

View full README

DOI

NeBULA2025

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 127 Channel types: eeg=127 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, AF7, AF3, AFz, F1, F5, FT7, FC3, C1, C5, TP7, CP3, P1, P5, PO7, PO3, POz, PO4, PO8, P6, P2, CPz, CP4, TP8, C6, C2, FC4, FT8, F6, AF8, AF4, F2, F9, AFF1h, FFC1h, FFC5h, FTT7h, FCC3h, CCP1h, CCP5h, TPP7h, P9, PPO9h, PO9, O9, OI1h, PPO1h, CPP3h, CPP4h, PPO2h, OI2h, O10, PO10, PPO10h, P10, TPP8h, CCP6h, CCP2h, FCC4h, FTT8h, FFC6h, FFC2h, AFF2h, F10, AFp1, AFF5h, FFT9h, FFT7h, FFC3h, FCC1h, FCC5h, FTT9h, TTP7h, CCP3h, CPP1h, CPP5h, TPP9h, POO9h, PPO5h, POO1, POO2, PPO6h, POO10h, TPP10h, CPP6h, CPP2h, CCP4h, TTP8h, FTT10h, FCC6h, FCC2h, FFC4h, FFT8h, FFT10h, AFF6h, AFp2 Montage: standard_1005 Hardware: Brain Products actiCHamp (128-channel actiCAP) Reference: FCz Ground: Fpz Sensor type: active electrodes Line frequency: 50.0 Hz Cap manufacturer: Brain Products Auxiliary channels: EMG (11 ch)

Participants

Number of subjects: 39 Health status: healthy Age: mean=44.6, std=13.2, min=25.0, max=71.0 Gender distribution: male=20, female=20 Handedness: right Species: human

Experimental Protocol

Paradigm: imagery Task type: motor execution Number of classes: 3 Class labels: reach_1, reach_2, reach_3 Study design: Standardized right-arm reaching to one of three illuminated targets (three movement types), performed under three assistance levels (free, low, high). Feedback type: none Stimulus type: target light Stimulus modalities: visual Primary modality: visual Synchronicity: cue-based Mode: offline Instructions: Seated participant reached a target when a light turned on with the right arm, at a normal pace, then returned to resting position with the hand on the right leg.

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser reach_1

     ├─ Sensory-event
     └─ Label/reach_1

reach_2

     ├─ Sensory-event
     └─ Label/reach_2

reach_3

├─ Sensory-event
└─ Label/reach_3

Paradigm-Specific Parameters

Detected paradigm: motor_imagery Imagery tasks: reach_1, reach_2, reach_3

Data Structure

Trials: 90 Trials context: ~10 trials per movement type per assistance-level run (3 classes x 3 runs per subject).

Tags

Pathology: Healthy Modality: Motor Type: motor_execution

Documentation

Description: NeBULA: high-density EEG and surface EMG recorded during a standardized upper-limb reaching task under three robotic assistance levels, for neuromechanical biomarker research. DOI: 10.1038/s41597-025-05042-4 License: CC-BY-4.0 Investigators: Federica Garro, Elisa Fenoglio, Ilaria Ceroni, Iryna Forsiuk, Michela Canepa, Marta Mozzon, Alessandro Bruschi, Fabio Zippo, Matteo Laffranchi, Lorenzo De Michieli, Stefano Buccelli, Michela Chiappalone, Marianna Semprini Institution: Istituto Italiano di Tecnologia Department: Rehab Technologies Address: Via Morego 30, Genova, Italy Country: IT Repository: Figshare Data URL: https://doi.org/10.1038/s41597-025-05042-4 Publication year: 2025 Funding: Istituto Nazionale Assicurazione Infortuni sul Lavoro (INAIL), project grant PR19-RR-P2

References

Garro, F., Fenoglio, E., Ceroni, I., Forsiuk, I., Canepa, M., Mozzon, M., Bruschi, A., Zippo, F., Laffranchi, M., De Michieli, L., Buccelli, S., Chiappalone, M., & Semprini, M. (2025). An EEG-EMG dataset from a standardized reaching task for biomarker research in upper limb assessment. Scientific Data. DOI: https://doi.org/10.1038/s41597-025-05042-4 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.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000322-blue)](https://doi.org/10.82901/nemar.nm000322) NeBULA2025 ========== Standardized reaching motor-execution EEG dataset (NeBULA) [1]_. Dataset Overview —————-

Code: NeBULA2025 Paradigm: imagery DOI: 10.1038/s41597-025-05042-4 Subjects: 39 Sessions per subject: 1 Events: reach_1=1, reach_2=2, reach_3=3 Trial interval: [0, 2] s Runs per session: 3 File format: BrainVision (BIDS)

Acquisition#

Sampling rate: 1000.0 Hz Number of channels: 127 Channel types: eeg=127 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, AF7, AF3, AFz, F1, F5, FT7, FC3, C1, C5, TP7, CP3, P1, P5, PO7, PO3, POz, PO4, PO8, P6, P2, CPz, CP4, TP8, C6, C2, FC4, FT8, F6, AF8, AF4, F2, F9, AFF1h, FFC1h, FFC5h, FTT7h, FCC3h, CCP1h, CCP5h, TPP7h, P9, PPO9h, PO9, O9, OI1h, PPO1h, CPP3h, CPP4h, PPO2h, OI2h, O10, PO10, PPO10h, P10, TPP8h, CCP6h, CCP2h, FCC4h, FTT8h, FFC6h, FFC2h, AFF2h, F10, AFp1, AFF5h, FFT9h, FFT7h, FFC3h, FCC1h, FCC5h, FTT9h, TTP7h, CCP3h, CPP1h, CPP5h, TPP9h, POO9h, PPO5h, POO1, POO2, PPO6h, POO10h, TPP10h, CPP6h, CPP2h, CCP4h, TTP8h, FTT10h, FCC6h, FCC2h, FFC4h, FFT8h, FFT10h, AFF6h, AFp2 Montage: standard_1005 Hardware: Brain Products actiCHamp (128-channel actiCAP) Reference: FCz Ground: Fpz Sensor type: active electrodes Line frequency: 50.0 Hz Cap manufacturer: Brain Products Auxiliary channels: EMG (11 ch)

Participants#

Number of subjects: 39 Health status: healthy Age: mean=44.6, std=13.2, min=25.0, max=71.0 Gender distribution: male=20, female=20 Handedness: right Species: human

Experimental Protocol#

Paradigm: imagery Task type: motor execution Number of classes: 3 Class labels: reach_1, reach_2, reach_3 Study design: Standardized right-arm reaching to one of three illuminated targets (three movement types), performed under three assistance levels (free, low, high). Feedback type: none Stimulus type: target light Stimulus modalities: visual Primary modality: visual Synchronicity: cue-based Mode: offline Instructions: Seated participant reached a target when a light turned on with the right arm, at a normal pace, then returned to resting position with the hand on the right leg.

HED Event Annotations#

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser reach_1

├─ Sensory-event └─ Label/reach_1

reach_2
├─ Sensory-event └─ Label/reach_2

reach_3
├─ Sensory-event └─ Label/reach_3

Paradigm-Specific Parameters#

Detected paradigm: motor_imagery Imagery tasks: reach_1, reach_2, reach_3

Data Structure#

Trials: 90 Trials context: ~10 trials per movement type per assistance-level run (3 classes x 3 runs per subject).

Tags#

Pathology: Healthy Modality: Motor Type: motor_execution

Documentation#

Description: NeBULA: high-density EEG and surface EMG recorded during a standardized upper-limb reaching task under three robotic assistance levels, for neuromechanical biomarker research. DOI: 10.1038/s41597-025-05042-4 License: CC-BY-4.0 Investigators: Federica Garro, Elisa Fenoglio, Ilaria Ceroni, Iryna Forsiuk, Michela Canepa, Marta Mozzon, Alessandro Bruschi, Fabio Zippo, Matteo Laffranchi, Lorenzo De Michieli, Stefano Buccelli, Michela Chiappalone, Marianna Semprini Institution: Istituto Italiano di Tecnologia Department: Rehab Technologies Address: Via Morego 30, Genova, Italy Country: IT Repository: Figshare Data URL: https://doi.org/10.1038/s41597-025-05042-4 Publication year: 2025 Funding: Istituto Nazionale Assicurazione Infortuni sul Lavoro (INAIL), project grant PR19-RR-P2

References#

Garro, F., Fenoglio, E., Ceroni, I., Forsiuk, I., Canepa, M., Mozzon, M., Bruschi, A., Zippo, F., Laffranchi, M., De Michieli, L., Buccelli, S., Chiappalone, M., & Semprini, M. (2025). An EEG-EMG dataset from a standardized reaching task for biomarker research in upper limb assessment. Scientific Data. DOI: https://doi.org/10.1038/s41597-025-05042-4 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.

License: CC-BY-4.0

Authors:

Federica Garro
Elisa Fenoglio
Ilaria Ceroni
Iryna Forsiuk
Michela Canepa
… and 8 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000322

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=39, range 45–45 yr, mean 44.0 yr)

40

Other · 39

Channel counts: 127 ch (n=117 recordings)

Sampling frequencies: 1000.0 Hz (n=117 recordings)

Total recording duration: 10 h 38 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 127 ch · EEG · 1000 Hz · 48 subjects, 234 recordings

Live trace viewer — sub-1 · ses-0 · task-imagery · run-2

Showing one representative recording out of 48 subjects and 234 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 · 125 sensors — 125 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.NM000322(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000322

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

class eegdash.dataset.NM000322(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

NeBULA2025: Standardized reaching motor-execution EEG dataset (NeBULA)

Study:: nm000322 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000322.

Modality: eeg; Subject type: Unknown. Subjects: 48; recordings: 234; tasks: 4.

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/nm000322 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000322 DOI: https://doi.org/10.82901/nemar.nm000322

Examples

>>> from eegdash.dataset import NM000322
>>> dataset = NM000322(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 — NM000322.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 nm000322 to reproduce the tutorial on this dataset.

Citation

Federica Garro, Elisa Fenoglio, Ilaria Ceroni, Iryna Forsiuk, Michela Canepa, … (2025). NeBULA2025: Standardized reaching motor-execution EEG dataset (NeBULA). 10.82901/nemar.nm000322

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000322.

Related & sibling datasets

NM000295EEG · 29 subj NM000349EEG · 39 subj NM000309EEG · 18 subj NM000330EEG · 22 subj NM000280EEG · 47 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.nm000322

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000322`
Title	NeBULA2025: Standardized reaching motor-execution EEG dataset (NeBULA)
Author (year)	—
Canonical	—
Importable as	`NM000322`
Year	2025
Authors	Federica Garro, Elisa Fenoglio, Ilaria Ceroni, Iryna Forsiuk, Michela Canepa, Marta Mozzon, Alessandro Bruschi, Fabio Zippo, Matteo Laffranchi, Lorenzo De Michieli, Stefano Buccelli, Michela Chiappalone, Marianna Semprini
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000322
Source links	OpenNeuro \| NeMAR \| Source URL

NM000322: eeg dataset, 48 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

Paradigm-Specific Parameters#

Data Structure#

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#