EEGdash›NeMAR›NM000316

Iss. 316 · 5 subjects · 25 recordings · CC-BY-4.0

Dataset Brief · IMUMIA2026

NM000316: eeg dataset, 5 subjects#

IMUMIA2026: Multi-paradigm motor-imagery EEG dataset (IMU-MI_A)

Access recordings and metadata through EEGDash.

Citation: Jianxiu Li, Changming Wang, Chao Chen (2026). IMUMIA2026: Multi-paradigm motor-imagery EEG dataset (IMU-MI_A). 10.82901/nemar.nm000316

Modality: eeg Subjects: 5 Recordings: 25 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

5-participant EEG dataset — IMUMIA2026: Multi-paradigm motor-imagery EEG dataset (IMU-MI_A).

Data & curation Jianxiu Li · Changming Wang · Chao Chen
Senior author Chao Chen
Year 2026 · Distributed via NeMAR

EEG · 64 ch1000 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 NM000316

dataset = NM000316(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 = NM000316(cache_dir="./data", subject="01")

Advanced query

dataset = NM000316(
    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{nm000316,
  title = {IMUMIA2026: Multi-paradigm motor-imagery EEG dataset (IMU-MI_A)},
  author = {Jianxiu Li and Changming Wang and Chao Chen},
  doi = {10.82901/nemar.nm000316},
  url = {https://doi.org/10.82901/nemar.nm000316},
}

§ 02Study · The README

About This Dataset#

Multi-paradigm motor-imagery EEG dataset (IMU-MI_A) [1]_.

Code: IMUMIA2026

Paradigm: imagery DOI: 10.5281/zenodo.20421767 Subjects: 5 Sessions per subject: 1 Events: left_hand=1, right_hand=2, left_foot=3, right_foot=4, left_thumb=5, right_thumb=6, left_index=7, right_index=8, left_pinch=9, right_pinch=10 Trial interval: [0, 4] s Runs per session: 5 File format: Curry

DOI

IMUMIA2026

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 69 Channel types: eeg=64, eog=2, ecg=1, emg=1, misc=1 Channel names: FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, T8, M1, TP7, CP5, CP3, CP1, CPZ, CP2, CP4, CP6, TP8, M2, P7, P5, P3, P1, PZ, P2, P4, P6, P8, PO7, PO5, PO3, POZ, PO4, PO6, PO8, CB1, O1, OZ, O2, CB2

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000316-blue)](https://doi.org/10.82901/nemar.nm000316) IMUMIA2026 ========== Multi-paradigm motor-imagery EEG dataset (IMU-MI_A) [1]_. Dataset Overview —————-

Code: IMUMIA2026 Paradigm: imagery DOI: 10.5281/zenodo.20421767 Subjects: 5 Sessions per subject: 1 Events: left_hand=1, right_hand=2, left_foot=3, right_foot=4, left_thumb=5, right_thumb=6, left_index=7, right_index=8, left_pinch=9, right_pinch=10 Trial interval: [0, 4] s Runs per session: 5 File format: Curry

Acquisition#

Sampling rate: 1000.0 Hz Number of channels: 69 Channel types: eeg=64, eog=2, ecg=1, emg=1, misc=1 Channel names: FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, T8, M1, TP7, CP5, CP3, CP1, CPZ, CP2, CP4, CP6, TP8, M2, P7, P5, P3, P1, PZ, P2, P4, P6, P8, PO7, PO5, PO3, POZ, PO4, PO6, PO8, CB1, O1, OZ, O2, CB2 Montage: standard_1020 Hardware: Neuroscan SynAmps (64-channel Quik-Cap) Reference: Cz Ground: forehead Sensor type: Ag/AgCl Line frequency: 50.0 Hz Impedance threshold: 10.0 kOhm Electrode type: passive Auxiliary channels: EOG (2 ch, HEO, VEO), EMG (1 ch), ECG

Participants#

Number of subjects: 5 Health status: healthy

Experimental Protocol#

Paradigm: imagery Number of classes: 10 Class labels: left_hand, right_hand, left_foot, right_foot, left_thumb, right_thumb, left_index, right_index, left_pinch, right_pinch Trial duration: 6.0 s Study design: Five motor-imagery tasks (left/right hand, foot, thumb, index finger and index-thumb pinch), each recorded under a Classic Arrow paradigm and a Cue-Execution dual-stage paradigm. Stimulus type: visual Stimulus modalities: visual Synchronicity: cue-based Mode: offline Instructions: Perform the cued left- or right-side motor imagery of the task’s body part following the arrow direction.

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

left_foot
├─ Sensory-event └─ Label/left_foot

right_foot
├─ Sensory-event └─ Label/right_foot

left_thumb
├─ Sensory-event └─ Label/left_thumb

right_thumb
├─ Sensory-event └─ Label/right_thumb

left_index
├─ Sensory-event └─ Label/left_index

right_index
├─ Sensory-event └─ Label/right_index

left_pinch
├─ Sensory-event └─ Label/left_pinch

right_pinch
├─ Sensory-event └─ Label/right_pinch

Paradigm-Specific Parameters#

Detected paradigm: motor_imagery Imagery tasks: left_hand, right_hand, left_foot, right_foot, left_thumb, right_thumb, left_index, right_index, left_pinch, right_pinch Imagery duration: 4.0 s

Data Structure#

Blocks per session: 5 Trials context: Single session with five runs (one motor-imagery task each). Only the Classic Arrow cues (12 trials per side and task) are labelled by default; the dual-stage paradigm markers are kept in the annotations.

Preprocessing#

Data state: raw Preprocessing applied: False

Tags#

Pathology: healthy Modality: motor Type: Motor Imagery

Documentation#

Description: Human motor-imagery EEG covering diverse cognitive states: five body-part tasks (hand, foot, thumb, index finger, pinch) under two paradigms, 64-channel Neuroscan at 1000 Hz. Public five-subject sample of a 244-participant dataset. DOI: 10.5281/zenodo.20421767 License: CC-BY-4.0 Investigators: Jianxiu Li, Changming Wang, Chao Chen Institution: Inner Mongolia University Address: Inner Mongolia, China Country: CN Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.20421767 Publication year: 2026 Keywords: EEG, motor imagery, BCI, fine motor imagery

References#

Li, J., Wang, C., and Chen, C. (2026). A Human Motor Imagery EEG Dataset Covering Diverse Cognitive States and Neural Response Patterns. Zenodo. DOI: https://doi.org/10.5281/zenodo.20421767 Notes The numeric event code to left/right assignment follows the authors’ documented condition order (the “Left … MI” condition is listed before the “Right … MI” condition in every task-Task*_events.json); the archive ships no explicit trigger code book. Users are advised to verify laterality against the EMG/EOG channels before publication. .. 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:

Jianxiu Li
Changming Wang
Chao Chen

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000316

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 64 ch (n=25 recordings)

Sampling frequencies: 1000.0 Hz (n=25 recordings)

Total recording duration: 2 h 23 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 1000 Hz · 5 subjects, 25 recordings

Live trace viewer — sub-1 · ses-0 · task-imagery · run-4

Showing one representative recording out of 5 subjects and 25 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 · 62 sensors — 62 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.NM000316(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000316

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

class eegdash.dataset.NM000316(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

IMUMIA2026: Multi-paradigm motor-imagery EEG dataset (IMU-MI_A)

Study:: nm000316 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000316.

Modality: eeg; Subject type: Unknown. Subjects: 5; recordings: 25; 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/nm000316 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000316 DOI: https://doi.org/10.82901/nemar.nm000316

Examples

>>> from eegdash.dataset import NM000316
>>> dataset = NM000316(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 — NM000316.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 nm000316 to reproduce the tutorial on this dataset.

Citation

Jianxiu Li, Changming Wang, Chao Chen (2026). IMUMIA2026: Multi-paradigm motor-imagery EEG dataset (IMU-MI_A). 10.82901/nemar.nm000316

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000316.

Related & sibling datasets

NM000150EEG NM000309EEG · 18 subj NM000330EEG · 22 subj NM000319EEG · 8 subj NM000295EEG · 29 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.nm000316

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000316`
Title	IMUMIA2026: Multi-paradigm motor-imagery EEG dataset (IMU-MI_A)
Author (year)	—
Canonical	—
Importable as	`NM000316`
Year	2026
Authors	Jianxiu Li, Changming Wang, Chao Chen
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000316
Source links	OpenNeuro \| NeMAR \| Source URL

NM000316: eeg dataset, 5 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

Paradigm-Specific Parameters#

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#