EEGdash›NeMAR›NM000335

Iss. 335 · 38 subjects · 1504 recordings · CC-BY-NC-4.0

Dataset Brief · Wang2026

NM000335: eeg dataset, 38 subjects#

Wang2026: Sensory-guided joint-learning MI-BCI study from Wang et al. (2026)

Access recordings and metadata through EEGDash.

Citation: Hanwen Wang, Yisha Zhang, Maxim Karrenbach, Yidan Ding, Bin He (2026). Wang2026: Sensory-guided joint-learning MI-BCI study from Wang et al. (2026). 10.82901/nemar.nm000335

Modality: eeg Subjects: 38 Recordings: 1504 License: CC-BY-NC-4.0 Source: nemar

Metadata: Complete (100%)

38-participant EEG dataset — Wang2026: Sensory-guided joint-learning MI-BCI study from Wang et al. (2026).

Data & curation Hanwen Wang · Yisha Zhang · Maxim Karrenbach · Yidan Ding · Bin He
Senior author Bin He
Year 2026 · Distributed via NeMAR
Funding NS124564 · NS131069 · + 2 more

EEG · 62 ch1000 HzBIDS 1.9.0Task · imagery9 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 NM000335

dataset = NM000335(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 = NM000335(cache_dir="./data", subject="01")

Advanced query

dataset = NM000335(
    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{nm000335,
  title = {Wang2026: Sensory-guided joint-learning MI-BCI study from Wang et al. (2026)},
  author = {Hanwen Wang and Yisha Zhang and Maxim Karrenbach and Yidan Ding and Bin He},
  doi = {10.82901/nemar.nm000335},
  url = {https://doi.org/10.82901/nemar.nm000335},
}

§ 02Study · The README

About This Dataset#

Sensory-guided joint-learning MI-BCI study from Wang et al. (2026).

Code: Wang2026

Paradigm: imagery DOI: 10.1184/R1/32293995.v1 Subjects: 39 Sessions per subject: 5 Events: left_hand=1, right_hand=2, hands=3, rest=4 Trial interval: [0.0, 4.999] s File format: MATLAB v7.3 Data preprocessed: True Contributing labs: Carnegie Mellon University Biomedical Functional Imaging and Neuroengineering Lab

DOI

Wang2026

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 62 Channel types: eeg=62 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, TP7, CP5, CP3, CP1, CPz, CP2, CP4, CP6, TP8, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO7, PO5, PO3, POz, PO4, PO6, PO8, CB1, O1, Oz, O2, CB2

View full README

DOI

Wang2026

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 62 Channel types: eeg=62 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, TP7, CP5, CP3, CP1, CPz, CP2, CP4, CP6, TP8, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO7, PO5, PO3, POz, PO4, PO6, PO8, CB1, O1, Oz, O2, CB2 Montage: standard_1005 Hardware: Compumedics Neuroscan SynAmps 2/RT Software: BCI2000 Reference: midway between Cz and CPz Line frequency: 60.0 Hz Online filters: {‘notch’: 60.0} Impedance threshold: 5.0 kOhm Cap manufacturer: Compumedics Neuroscan Cap model: Quik-Cap

Ethics

Every experiment involving human participants has been carried out following a protocol approved by the Institutional Review Board of Carnegie Mellon University. Each participant gave informed written consent. (Quoted verbatim from the primary publication’s Ethics statement: Wang, H., Zhang, Y., Karrenbach, M., Ding, Y., & He, B. (2026). Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control. Nature Communications, 17, 6177. https://doi.org/10.1038/s41467-026-75435-5. Participants were recruited from the Pittsburgh area and compensated $15/hour; informed consent followed the World Medical Association’s Declaration of Helsinki.)

Participants

Number of subjects: 39 Health status: healthy BCI experience: naive

Experimental Protocol

Paradigm: imagery Task type: online 1D and 2D cursor control Number of classes: 4 Class labels: left_hand, right_hand, hands, rest Trial duration: 5.0 s Tasks: left hand imagery, right hand imagery, bilateral hand imagery, rest Study design: One longitudinal MI-BCI study release with 39 unique participants. The primary randomized experiment included 31 participants: joint learning (n=15), BCI2000 control (n=8), and tactile control (n=8). A separately recruited EEGNet-control cohort added 8 participants. Feedback type: visual cursor feedback, with cohort-specific tactile guidance, EEGNet adaptation, or fixed BCI2000 decoding Stimulus type: visual cursor and directional cue Stimulus modalities: visual, tactile Primary modality: visual Synchronicity: synchronous Mode: online Training/test split: True Stimulus presentation: SoftwareName=BCI2000

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

hands

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine, Move, Hand

rest

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

Paradigm-Specific Parameters

Detected paradigm: motor_imagery Imagery tasks: left_hand, right_hand, hands, rest Imagery duration: 5.0 s

Data Structure

Trials: varies by run and subject Blocks per session: 6 Trials context: Each regular session contains six runs; run0 is a separate initial BCI2000 baseline. Subjects completed 4, 6, or 8 regular sessions.

Preprocessing

Data state: trial-segmented Preprocessing applied: True Steps: trial segmentation, auxiliary-channel exclusion, per-trial temporal centering in the MOABB reconstruction Notch filter: 60.0 Hz Notes: The release contains 62 selected EEG channels and states that no additional filtering or downsampling was applied. Runs use either fixed-length EEGNet trials or variable-length BCI2000 trials.

Signal Processing

Classifiers: weighted EEGNet, EEGNet, BCI2000 linear classifier Feature extraction: EEGNet, autoregressive spectral features Frequency bands: online_EEGNet=[4.0, 40.0] Hz Spatial filters: CSP-based initial sample weighting

BCI Application

Applications: cursor control Environment: laboratory Online feedback: True

Tags

Pathology: Healthy Modality: Motor Type: Motor Imagery, Online BCI

Documentation

DOI: 10.1184/R1/32293995.v1 Associated paper DOI: 10.1038/s41467-026-75435-5 License: CC-BY-NC-4.0 Investigators: Hanwen Wang, Yisha Zhang, Maxim Karrenbach, Yidan Ding, Bin He Senior author: Bin He Contact: bhe1@andrew.cmu.edu Institution: Carnegie Mellon University Department: Department of Biomedical Engineering Country: US Repository: KiltHub (Figshare) Data URL: https://kilthub.cmu.edu/articles/dataset/EEG_Datasets_for_Sensory-Guided_Joint_Learning_in_Motor_Imagery_Brain-Computer_Interfaces/32293995 Publication year: 2026 Funding: NS124564; NS131069; NS127849; NS096761 Keywords: motor imagery, BCI, joint learning, tactile stimulation, EEGNet

External Links

Source: https://kilthub.cmu.edu/articles/dataset/EEG_Datasets_for_Sensory-Guided_Joint_Learning_in_Motor_Imagery_Brain-Computer_Interfaces/32293995 Paper: https://doi.org/10.1038/s41467-026-75435-5 Code: bfinl/SensoryGuidedJointLearning

References

Wang, H., Zhang, Y., Karrenbach, M., Ding, Y., & He, B. (2026). Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control. Nature Communications, 17, 6177. https://doi.org/10.1038/s41467-026-75435-5 Wang, H., Zhang, Y., Karrenbach, M., Ding, Y., & He, B. (2026). EEG Datasets for Sensory-Guided Joint Learning in Motor Imagery Brain-Computer Interfaces. Carnegie Mellon University. https://doi.org/10.1184/R1/32293995.v1 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

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000335-blue)](https://doi.org/10.82901/nemar.nm000335) Wang2026 ======== Sensory-guided joint-learning MI-BCI study from Wang et al. (2026). Dataset Overview —————-

Code: Wang2026 Paradigm: imagery DOI: 10.1184/R1/32293995.v1 Subjects: 39 Sessions per subject: 5 Events: left_hand=1, right_hand=2, hands=3, rest=4 Trial interval: [0.0, 4.999] s File format: MATLAB v7.3 Data preprocessed: True Contributing labs: Carnegie Mellon University Biomedical Functional Imaging and Neuroengineering Lab

Acquisition#

Sampling rate: 1000.0 Hz Number of channels: 62 Channel types: eeg=62 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, TP7, CP5, CP3, CP1, CPz, CP2, CP4, CP6, TP8, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO7, PO5, PO3, POz, PO4, PO6, PO8, CB1, O1, Oz, O2, CB2 Montage: standard_1005 Hardware: Compumedics Neuroscan SynAmps 2/RT Software: BCI2000 Reference: midway between Cz and CPz Line frequency: 60.0 Hz Online filters: {‘notch’: 60.0} Impedance threshold: 5.0 kOhm Cap manufacturer: Compumedics Neuroscan Cap model: Quik-Cap

Ethics#

Every experiment involving human participants has been carried out following a protocol approved by the Institutional Review Board of Carnegie Mellon University. Each participant gave informed written consent. (Quoted verbatim from the primary publication’s Ethics statement: Wang, H., Zhang, Y., Karrenbach, M., Ding, Y., & He, B. (2026). Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control. Nature Communications, 17, 6177. https://doi.org/10.1038/s41467-026-75435-5. Participants were recruited from the Pittsburgh area and compensated $15/hour; informed consent followed the World Medical Association’s Declaration of Helsinki.)

Participants#

Number of subjects: 39 Health status: healthy BCI experience: naive

Experimental Protocol#

Paradigm: imagery Task type: online 1D and 2D cursor control Number of classes: 4 Class labels: left_hand, right_hand, hands, rest Trial duration: 5.0 s Tasks: left hand imagery, right hand imagery, bilateral hand imagery, rest Study design: One longitudinal MI-BCI study release with 39 unique participants. The primary randomized experiment included 31 participants: joint learning (n=15), BCI2000 control (n=8), and tactile control (n=8). A separately recruited EEGNet-control cohort added 8 participants. Feedback type: visual cursor feedback, with cohort-specific tactile guidance, EEGNet adaptation, or fixed BCI2000 decoding Stimulus type: visual cursor and directional cue Stimulus modalities: visual, tactile Primary modality: visual Synchronicity: synchronous Mode: online Training/test split: True Stimulus presentation: SoftwareName=BCI2000

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

hands
├─ Sensory-event, Experimental-stimulus, Visual-presentation └─ Agent-action

└─ Imagine, Move, Hand

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

Paradigm-Specific Parameters#

Detected paradigm: motor_imagery Imagery tasks: left_hand, right_hand, hands, rest Imagery duration: 5.0 s

Data Structure#

Trials: varies by run and subject Blocks per session: 6 Trials context: Each regular session contains six runs; run0 is a separate initial BCI2000 baseline. Subjects completed 4, 6, or 8 regular sessions.

Preprocessing#

Data state: trial-segmented Preprocessing applied: True Steps: trial segmentation, auxiliary-channel exclusion, per-trial temporal centering in the MOABB reconstruction Notch filter: 60.0 Hz Notes: The release contains 62 selected EEG channels and states that no additional filtering or downsampling was applied. Runs use either fixed-length EEGNet trials or variable-length BCI2000 trials.

Signal Processing#

Classifiers: weighted EEGNet, EEGNet, BCI2000 linear classifier Feature extraction: EEGNet, autoregressive spectral features Frequency bands: online_EEGNet=[4.0, 40.0] Hz Spatial filters: CSP-based initial sample weighting

BCI Application#

Applications: cursor control Environment: laboratory Online feedback: True

Tags#

Pathology: Healthy Modality: Motor Type: Motor Imagery, Online BCI

Documentation#

DOI: 10.1184/R1/32293995.v1 Associated paper DOI: 10.1038/s41467-026-75435-5 License: CC-BY-NC-4.0 Investigators: Hanwen Wang, Yisha Zhang, Maxim Karrenbach, Yidan Ding, Bin He Senior author: Bin He Contact: bhe1@andrew.cmu.edu Institution: Carnegie Mellon University Department: Department of Biomedical Engineering Country: US Repository: KiltHub (Figshare) Data URL: https://kilthub.cmu.edu/articles/dataset/EEG_Datasets_for_Sensory-Guided_Joint_Learning_in_Motor_Imagery_Brain-Computer_Interfaces/32293995 Publication year: 2026 Funding: NS124564; NS131069; NS127849; NS096761 Keywords: motor imagery, BCI, joint learning, tactile stimulation, EEGNet

External Links#

Source: https://kilthub.cmu.edu/articles/dataset/EEG_Datasets_for_Sensory-Guided_Joint_Learning_in_Motor_Imagery_Brain-Computer_Interfaces/32293995 Paper: https://doi.org/10.1038/s41467-026-75435-5 Code: bfinl/SensoryGuidedJointLearning

References#

Wang, H., Zhang, Y., Karrenbach, M., Ding, Y., & He, B. (2026). Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control. Nature Communications, 17, 6177. https://doi.org/10.1038/s41467-026-75435-5 Wang, H., Zhang, Y., Karrenbach, M., Ding, Y., & He, B. (2026). EEG Datasets for Sensory-Guided Joint Learning in Motor Imagery Brain-Computer Interfaces. Carnegie Mellon University. https://doi.org/10.1184/R1/32293995.v1 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

License: CC-BY-NC-4.0

Authors:

Hanwen Wang
Yisha Zhang
Maxim Karrenbach
Yidan Ding
Bin He

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000335

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 62 ch (n=1504 recordings)

Sampling frequencies: 1000.0 Hz (n=1504 recordings)

Total recording duration: 91 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 62 ch · EEG · 1000 Hz · 38 subjects, 1504 recordings

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

Showing one representative recording out of 38 subjects and 1504 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 · 60 sensors — 60 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.NM000335(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000335

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

class eegdash.dataset.NM000335(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Wang2026: Sensory-guided joint-learning MI-BCI study from Wang et al. (2026)

Study:: nm000335 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000335.

Modality: eeg; Subject type: Unknown. Subjects: 38; recordings: 1504; 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/nm000335 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000335 DOI: https://doi.org/10.82901/nemar.nm000335

Examples

>>> from eegdash.dataset import NM000335
>>> dataset = NM000335(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 — NM000335.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 nm000335 to reproduce the tutorial on this dataset.

Citation

Hanwen Wang, Yisha Zhang, Maxim Karrenbach, Yidan Ding, Bin He (2026). Wang2026: Sensory-guided joint-learning MI-BCI study from Wang et al. (2026). 10.82901/nemar.nm000335

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000335.

Related & sibling datasets

ON007327EEG · 35 subj NM000304EEG · 54 subj NM000150EEG ON007180EEG · 25 subj ON002791EEG · 23 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-NC-4.0 · 10.82901/nemar.nm000335

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000335`
Title	Wang2026: Sensory-guided joint-learning MI-BCI study from Wang et al. (2026)
Author (year)	—
Canonical	—
Importable as	`NM000335`
Year	2026
Authors	Hanwen Wang, Yisha Zhang, Maxim Karrenbach, Yidan Ding, Bin He
License	CC-BY-NC-4.0
Citation / DOI	10.82901/nemar.nm000335
Source links	OpenNeuro \| NeMAR \| Source URL

NM000335: eeg dataset, 38 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Ethics#

Participants#

Experimental Protocol#

HED Event Annotations#

Paradigm-Specific Parameters#

Data Structure#

Preprocessing#

Signal Processing#

BCI Application#

Tags#

Documentation#

External Links#

References#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#