moabb.datasets.Wang2026Handwriting#
- class moabb.datasets.Wang2026Handwriting(subjects=None, sessions=None, *, return_all_modalities=False)[source]#
Bases:
BaseBIDSDataset[source]Dataset Snapshot
Wang2026Handwriting
Cognitive, 11 classes
Cognitive Code: Wang2026Handwriting 21 subjects 2 sessions 32 ch 1000 Hz 11 classes 4.0 s trials CC BY 4.0Class Labels: horizontal, vertical, left_falling, right_falling, turning, a, o, e, ...
Citation & Impact
- Paper DOI10.1038/s41597-026-06708-3
- CitationsLoading…
- Public APICrossref | OpenAlex
Stimulus Protocol
4s task window per trial · 11-class cognitive paradigm · 2 runs/session across 2 sessions
HED Event TagsHED tagsSource: MOABB BIDS HED annotation mapping.
horizontalSensory-eventLabelverticalSensory-eventLabelleft_fallingSensory-eventLabelright_fallingSensory-eventLabelturningSensory-eventLabelaSensory-eventLabeloSensory-eventLabeleSensory-eventLabeliSensory-eventLabeluSensory-eventLabelvSensory-eventLabelHED tree view
Tree · horizontal
├─ Sensory-event └─ Label
Tree · vertical
├─ Sensory-event └─ Label
Tree · left_falling
├─ Sensory-event └─ Label
Tree · right_falling
├─ Sensory-event └─ Label
Tree · turning
├─ Sensory-event └─ Label
Tree · a
├─ Sensory-event └─ Label
Tree · o
├─ Sensory-event └─ Label
Tree · e
├─ Sensory-event └─ Label
Tree · i
├─ Sensory-event └─ Label
Tree · u
├─ Sensory-event └─ Label
Tree · v
├─ Sensory-event └─ Label
Channel SummaryTotal channels32EEG32 (Ag/AgCl)Montage10-05Sampling1000 HzReferenceCPzNotch / line50 HzThis diagram is automatically generated from MOABB metadata. Please consult the original publication to confirm the experimental protocol details.
Chinese handwriting imagery dataset (CCS-HI / SV-HI) from Wang et al. 2026 (cognitive paradigm).
Dataset from [1].
The dataset contains EEG recordings from 21 healthy, right-handed adults (10 female, 11 male, aged 22-32 years) performing two handwriting-imagery tasks:
CCS-HI (Chinese character strokes imagery): five stroke classes (horizontal, vertical, left-falling, right-falling, turning), 40 trials per class;
SV-HI (single vowels imagery): six Pinyin vowel classes (a, o, e, i, u, ü), 40 trials per class.
Each subject completed both tasks in each of two sessions recorded on separate days. Every trial is a 4 s window of kinesthetic handwriting imagery cued by a visual stimulus, followed by a rest period. EEG was recorded from 32 Ag/AgCl electrodes (10-10 layout) at 1000 Hz, referenced to CPz with Fpz as ground, with a 0.1-100 Hz hardware band-pass and no software filtering applied before disclosure.
The subjects split into expert and novice groups by their handwriting imagery performance (see the
groupcolumn of the release’sparticipants.tsv), which makes the dataset usable for both within-session and expert/novice transfer analyses.Important
The release flags two sessions with fewer valid trials than the protocol’s standard count (sub-06 session 2 of SV-HI: 225 instead of 240; sub-13 session 2 of CCS-HI: 162 instead of 200). The authors attribute this to trigger misfires during acquisition. MOABB keeps those sessions as released; the counts above describe the standard session, and the per-subject trial totals reported by
get_data()reflect what each session actually contains.This loader reads the authors’ BIDS release hosted on figshare. Each subject is distributed as a single zip containing both tasks for both sessions; the loader downloads it, unpacks it beside the other BIDS sidecars it fetches from the same deposit, and reads the resulting BIDS tree with
mne_bids. Sessions are numbered"0"and"1"in MOABB and runs are named after the BIDS task (CCSHI/SVHI).References
[1]F. Wang, Y. Chen, P. Wang, A. Gong, H. Xu, and Y. Fu, “An EEG dataset for handwriting imagery decoding of Chinese character strokes and Pinyin single vowels,” Scientific Data, 2026. DOI: 10.1038/s41597-026-06708-3
from moabb.datasets import Wang2026Handwriting dataset = Wang2026Handwriting() data = dataset.get_data(subjects=[1]) print(data[1])
Dataset summary
#Subj
21
#Chan
32
#Classes
5
#Trials / class
40
Trials length
4 s
Freq
1000 Hz
#Sessions
2
#Runs
2
Total_trials
18480
Participants
Population: healthy
Age: 25.7619 (range: 22-32) years
Handedness: {‘right’: 21}
BCI experience: mixed (expert and novice groups)
Equipment
Amplifier: Neuracle NeuSenW32 32-channel Ag/AgCl cap
Electrodes: Ag/AgCl
Montage: standard_1005
Reference: CPz
Preprocessing
Data state: raw
Bandpass filter: 0.1-100 Hz
Notes: Recorded with a 0.1-100 Hz hardware band-pass; the release applies no software filter.
Data Access
DOI: 10.1038/s41597-026-06708-3
Repository: figshare
Experimental Protocol
Paradigm: cognitive
Task type: handwriting_imagery
Feedback: none
Stimulus: visual cue (stroke or vowel to imagine writing)
Notes
Added in version 1.8.0.
- __init__(subjects=None, sessions=None, *, return_all_modalities=False)[source]#
Initialize function for the BaseDataset.
- property all_subjects#
Full list of subjects available in this dataset (unfiltered).
- convert_to_bids(path=None, subjects=None, overwrite=False, format='EDF', verbose=None, generate_figures=False)[source]#
Convert the dataset to BIDS format.
Saves the raw EEG data in a BIDS-compliant directory structure. Unlike the caching mechanism (see
CacheConfig), the files produced here do not contain a processing-pipeline hash (desc-<hash>) in their names, making the output a clean, shareable BIDS dataset.- Parameters:
path (str |
Path| None) – Directory under which the BIDS dataset will be written. IfNonethe default MNE data directory is used (same default as the rest of MOABB).subjects (list of int | None) – Subject numbers to convert. If
None, all subjects insubject_listare converted.overwrite (bool) – If
True, existing BIDS files for a subject are removed before saving. Default isFalse.format (str) – The file format for the raw EEG data. Supported values are
"EDF"(default),"BrainVision", and"EEGLAB".verbose (str | None) – Verbosity level forwarded to MNE/MNE-BIDS.
generate_figures (bool) – If
True, generate interactive neural signature HTML figures in{bids_root}/derivatives/neural_signatures/. Requiresplotly(pip install moabb[interactive]). Default isFalse.
- Returns:
bids_root – Path to the root of the written BIDS dataset.
- Return type:
Examples
>>> from moabb.datasets import AlexMI >>> dataset = AlexMI() >>> bids_root = dataset.convert_to_bids(path="/tmp/bids", subjects=[1])
Notes
Use
CacheConfigto configure caching forget_data(). Usemoabb.datasets.bids_interface.get_bids_rootto get the BIDS root path.Added in version 1.5.
- data_path(subject, path=None, force_update=False, update_path=None, verbose=None)[source]#
Get path to local copy of a subject data.
- Parameters:
subject (int) – Number of subject to use
path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter
MNE_DATASETS_(dataset)_PATHis used. If it doesn’t exist, the “~/mne_data” directory is used. If the dataset is not found under the given path, the data will be automatically downloaded to the specified folder.force_update (bool) – Force update of the dataset even if a local copy exists.
update_path (bool | None Deprecated) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted.
verbose (bool, str, int, or None) – If not None, override default verbose level (see
mne.verbose()).
- Returns:
path – Local path to the given data file. This path is contained inside a list of length one, for compatibility.
- Return type:
- download(subject_list=None, path=None, force_update=False, update_path=None, accept=False, verbose=None)[source]#
Download the BIDS trees of the requested subjects.
- get_additional_metadata(subject, session, run)[source]#
Load additional metadata for a specific subject, session, and run.
- Parameters:
- Returns:
A DataFrame containing the additional metadata if available, otherwise None.
- Return type:
None |
pandas.DataFrame
- get_block_repetition(paradigm, subjects, block_list, repetition_list)[source]#
Select data for all provided subjects, blocks and repetitions.
subject -> session -> run -> block -> repetition
See also
- get_data(subjects=None, cache_config=None, process_pipeline=None, n_jobs=1)[source]#
Return the data corresponding to a list of subjects.
The returned data is a dictionary with the following structure:
data = {"subject_id": {"session_id": {"run_id": run}}}
subjects are on top, then we have sessions, then runs. A sessions is a recording done in a single day, without removing the EEG cap. A session is constitued of at least one run. A run is a single contiguous recording. Some dataset break session in multiple runs.
Processing steps can optionally be applied to the data using the
*_pipelinearguments. These pipelines are applied in the following order:raw_pipeline->epochs_pipeline->array_pipeline. If a*_pipelineargument isNone, the step will be skipped. Therefore, thearray_pipelinemay either receive amne.io.Rawor amne.Epochsobject as input depending on whetherepochs_pipelineisNoneor not.- Parameters:
subjects (List of int) – List of subject number
cache_config (dict |
CacheConfig) – Configuration for caching of datasets. SeeCacheConfigfor details.process_pipeline (
sklearn.pipeline.Pipeline| None) – Optional processing pipeline to apply to the data. To generate an adequate pipeline, we recommend usingmoabb.make_process_pipelines(). This pipeline will receivemne.io.BaseRawobjects. The steps names of this pipeline should be elements ofStepType. According to their name, the steps should either return amne.io.BaseRaw, amne.Epochs, or anumpy.ndarray. This pipeline must be “fixed” because it will not be trained, i.e. no call tofitwill be made.n_jobs (int) – Number of jobs to run in parallel over subjects (passed to
joblib.Parallel). Default1(sequential). Per-subject processing (reading, filtering, resampling, epoching) is independent, so this gives a near-linear speedup for datasets with many subjects.
- Returns:
data – dict containing the raw data
- Return type:
Dict
- property metadata[source]#
Return structured metadata for this dataset.
Returns the DatasetMetadata object from the centralized catalog, or None if metadata is not available for this dataset.
- Returns:
The metadata object containing acquisition parameters, participant demographics, experiment details, and documentation. Returns None if no metadata is registered for this dataset.
- Return type:
DatasetMetadata| None
Examples
>>> from moabb.datasets import BNCI2014_001 >>> dataset = BNCI2014_001() >>> dataset.metadata.participants.n_subjects 9 >>> dataset.metadata.acquisition.sampling_rate 250.0
- sourcedata_path(subject=None, path=None, force_update=False, verbose=None)[source]#
Get the dataset’s original pre-BIDS distribution from NEMAR.
Where
data_path()fetches the original files from the upstream host, this fetches the copy NEMAR mirrors undersourcedata/. The files keep their upstream names, so the two are interchangeable in content – but NEMAR stays reachable when the upstream host is slow, rate-limited, behind a bot gate, or retired.- Parameters:
subject (int | str | None) – Restrict the download to one subject, resolved through the deposit’s
sourcedata_provenance.json. Deposits enriched before that manifest recorded subjects fall back to the whole tree with a warning. WhenNonethe whole tree is fetched.path (None | str) – Base path where MOABB stores datasets.
force_update (bool) – Re-fetch even when a local copy is present.
verbose (bool, str, int, or None) – If not None, override default verbose level.
- Returns:
Local path to the
sourcedatadirectory.- Return type:
- Raises:
ValueError – If the dataset declares no
nemar_id.moabb.datasets.download.NemarDownloadError – If the download fails or the deposit publishes no
sourcedata/.