EEGdash›NeMAR›NM000177

Iss. 177 · 18 subjects · 360 recordings · CC-BY-4.0

Dataset Brief · Multi-session longitudinal motor imagery EEG dataset (Kumar e…

NM000177: eeg dataset, 18 subjects#

Multi-session longitudinal motor imagery EEG dataset (Kumar et al. 2024)

Access recordings and metadata through EEGDash.

Citation: Satyam Kumar, Hussein Alawieh, Frigyes Samuel Racz, Rawan Fakhreddine, Jose del R. Millan (20). Multi-session longitudinal motor imagery EEG dataset (Kumar et al. 2024). 10.82901/nemar.nm000177

Modality: eeg Subjects: 18 Recordings: 360 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

18-participant EEG dataset — Multi-session longitudinal motor imagery EEG dataset (Kumar et al. 2024).

Data & curation Satyam Kumar · Hussein Alawieh · Frigyes Samuel Racz · Rawan Fakhreddine · Jose del R. Millan
Senior author Jose del R. Millan
Year 20 · Distributed via NeMAR
Funding Coleman Fung Foundation · Sinclair Foundation

EEG · 22 ch512 HzBIDS 1.9.0Task · imagery6 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 NM000177

dataset = NM000177(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 = NM000177(cache_dir="./data", subject="01")

Advanced query

dataset = NM000177(
    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{nm000177,
  title = {Multi-session longitudinal motor imagery EEG dataset (Kumar et al. 2024)},
  author = {Satyam Kumar and Hussein Alawieh and Frigyes Samuel Racz and Rawan Fakhreddine and Jose del R. Millan},
  doi = {10.82901/nemar.nm000177},
  url = {https://doi.org/10.82901/nemar.nm000177},
}

§ 02Study · The README

About This Dataset#

A longitudinal motor imagery EEG dataset from 18 BCI-naive subjects across 6 sessions (1 offline + 5 online) demonstrating transfer learning for brain-computer interface skill acquisition. The study compares two domain adaptation frameworks—Generic Recentering (unsupervised) and Personally Assisted Recentering (supervised)—for calibration-free BCI training using left/right hand motor imagery with visual feedback. Data were acquired at 512 Hz using 22 EEG channels and processed with Riemannian geometry-based classifiers.

EEG data were recorded from 18 healthy subjects (age mean=23.22±3.59 years) across 6 sessions using ANT Neuro eego mylab hardware with 22 EEG channels and 3 EOG channels at 512 Hz sampling rate. Subjects performed cue-based left/right hand motor imagery tasks with continuous visual feedback across bar-feedback runs and car racing games. Session 1 consisted of 4 offline runs (80 trials); sessions 2-6 included 3-4 online runs each. EEG signals were bandpass filtered at 8-30 Hz using second-order Butterworth filters. Features were extracted as covariance matrices and classified using Riemannian Minimum Distance to Mean decoder.

DOI

Multi-session longitudinal motor imagery EEG dataset (Kumar et al. 2024)

Overview

How to Access via MOABB

Install MOABB and load this dataset directly:

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000177-blue)](https://doi.org/10.82901/nemar.nm000177) # Multi-session longitudinal motor imagery EEG dataset (Kumar et al. 2024) ## Overview A longitudinal motor imagery EEG dataset from 18 BCI-naive subjects across 6 sessions (1 offline + 5 online) demonstrating transfer learning for brain-computer interface skill acquisition. The study compares two domain adaptation frameworks—Generic Recentering (unsupervised) and Personally Assisted Recentering (supervised)—for calibration-free BCI training using left/right hand motor imagery with visual feedback. Data were acquired at 512 Hz using 22 EEG channels and processed with Riemannian geometry-based classifiers. ## Dataset Summary | Property | Value | |---|—| | Subjects | 18 | | Channels | 22 | | Classes | 2 | | Trial length | 5 s | | Sampling frequency | 512 Hz | | Sessions | 6 | | Total trials | 7156 | | Paradigm | MotorImagery | ## Data Collection Methods EEG data were recorded from 18 healthy subjects (age mean=23.22±3.59 years) across 6 sessions using ANT Neuro eego mylab hardware with 22 EEG channels and 3 EOG channels at 512 Hz sampling rate. Subjects performed cue-based left/right hand motor imagery tasks with continuous visual feedback across bar-feedback runs and car racing games. Session 1 consisted of 4 offline runs (80 trials); sessions 2-6 included 3-4 online runs each. EEG signals were bandpass filtered at 8-30 Hz using second-order Butterworth filters. Features were extracted as covariance matrices and classified using Riemannian Minimum Distance to Mean decoder. ## How to Access via MOABB Install MOABB and load this dataset directly: `python from moabb.datasets import Kumar2024 from moabb.paradigms import MotorImagery paradigm = MotorImagery() dataset = Kumar2024() X, y, metadata = paradigm.get_data(dataset) ` For more details see the [MOABB documentation](https://moabb.neurotechx.com/) and the [MOABB dataset page](https://moabb.neurotechx.com/docs/generated/moabb.datasets.Kumar2024.html). ## Citation If you use this dataset please cite the primary publication: > DOI: [10.1093/pnasnexus/pgae076](https://doi.org/10.1093/pnasnexus/pgae076) ## NEMAR / MOABB Benchmark Collection This BIDS-formatted dataset was converted from the original data using the [MOABB](https://moabb.neurotechx.com/) pipeline and re-hosted on [NEMAR](https://nemar.org/) as part of the MOABB benchmark collection. The original data and license terms apply — see dataset_description.json for details.

License: CC-BY-4.0

Authors:

Satyam Kumar
Hussein Alawieh
Frigyes Samuel Racz
Rawan Fakhreddine
Jose del R. Millan

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000177

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=18, range 2020–2020 yr)

2020

Other · 18

Channel counts: 22 ch (n=360 recordings)

Sampling frequencies: 512.0 Hz (n=360 recordings)

Total recording duration: 21 h 51 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 22 ch · EEG · 512 Hz · 18 subjects, 360 recordings

Live trace viewer — sub-17 · ses-3 · task-imagery · run-1

Showing one representative recording out of 18 subjects and 360 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 · 22 sensors — 22 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.NM000177(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000177

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

class eegdash.dataset.NM000177(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Multi-session longitudinal motor imagery EEG dataset (Kumar et al. 2024)

Study:: nm000177 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000177.

Modality: eeg; Subject type: Unknown. Subjects: 18; recordings: 360; 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/nm000177 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000177 DOI: https://doi.org/10.82901/nemar.nm000177

Examples

>>> from eegdash.dataset import NM000177
>>> dataset = NM000177(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 — NM000177.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 nm000177 to reproduce the tutorial on this dataset.

Citation

Satyam Kumar, Hussein Alawieh, Frigyes Samuel Racz, Rawan Fakhreddine, Jose del R. Millan (20). Multi-session longitudinal motor imagery EEG dataset (Kumar et al. 2024). 10.82901/nemar.nm000177

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000177.

Related & sibling datasets

ON005185EEG · 20 subj ON005811EEG · 19 subj ON004408EEG · 19 subj ON003190EEG · 19 subj ON001971EEG · 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.nm000177

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000177`
Title	Multi-session longitudinal motor imagery EEG dataset (Kumar et al. 2024)
Author (year)	—
Canonical	—
Importable as	`NM000177`
Year	20
Authors	Satyam Kumar, Hussein Alawieh, Frigyes Samuel Racz, Rawan Fakhreddine, Jose del R. Millan
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000177
Source links	OpenNeuro \| NeMAR \| Source URL

NM000177: eeg dataset, 18 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#