moabb.datasets.Shin2022#
- class moabb.datasets.Shin2022(subjects=None, sessions=None, *, return_all_modalities=False)[source]#
Bases:
BaseDataset[source]Dataset Snapshot
Shin2022
Imagery, 2 classes (right_hand vs left_hand)
Class Labels: right_hand, left_hand
Citation & Impact
- Paper DOI10.3389/fnhum.2022.951591
- CitationsLoading…
- Public APICrossref | OpenAlex
Stimulus Protocol
6s task window per trial · 2-class imagery paradigm · 11 runs/session across 1 sessions
HED Event TagsHED tagsSource: MOABB BIDS HED annotation mapping.
right_handSensory-eventAgent-actionleft_handSensory-eventAgent-actionHED tree view
Tree · right_hand
├─ Sensory-event │ ├─ Experimental-stimulus │ └─ Visual-presentation └─ Agent-action └─ Imagine ├─ Move └─ Right └─ HandTree · left_hand
├─ Sensory-event │ ├─ Experimental-stimulus │ └─ Visual-presentation └─ Agent-action └─ Imagine ├─ Move └─ Left └─ HandChannel SummaryTotal channels16EEG16 (dry)Montage10-05Sampling250 HzReferenceunknownFilterbroadband (online 0.5-30 Hz filter disabled at source)Notch / line60 HzThis diagram is automatically generated from MOABB metadata. Please consult the original publication to confirm the experimental protocol details.
Closed-loop 1D left/right motor imagery EEG dataset (live recordings).
Dataset from [1] (data record [2]). Ten healthy adults performed a 1D left/right center-out sensorimotor-rhythm cursor task under continuous visual feedback, recorded with a 16-channel dry-electrode g.tec g.Nautilus at 250 Hz through BCI2000. Each subject has one session of 11 runs of 24 trials (12 per class), one run per value of an online control parameter: normalization bin width (
BW30..``BW120``), maximum cursor velocity (CV200..``CV350``) and trials carried for normalization (NT0,NT24,NT48). These only change the online dynamics, not the task.Labels are data-borne: the cued class is the BCI2000
TargetCodestate and events are placed at the onset of theFeedback(active control) period, whose variable length (target hit or 6 s timeout) is stored as the annotation duration. Only the recordedLIVEEEG is loaded; the archive’sSIMULATEDtree (synthetic cursor telemetry in.csv) is not EEG.Reading the
.datfiles requirespip install "moabb[bci2000]".Paper-vs-release note: the paper describes each session as “10 runs of 24 1D LR center-out discrete trials”, because “the ‘NT = 0 trials’ run … was jointly represented by the ‘BW = 60 s (default)’ run, thus forming 10 runs (4 + 2 + 4)”. The archive’s
NTfolder is documented here with three files (NT0,NT24,NT48), i.e. 11 runs; the loader enumerates whatever.datfiles the archive holds, so the declared 11 runs / 2640 trials remain to be confirmed against the release. The online pipeline used a “Notch filtered 58-62 Hz” stage (60 Hz mains).References
[1]H. Shin, D. Suma and B. He, “Closed-loop motor imagery EEG simulation for brain-computer interfaces,” Frontiers in Human Neuroscience, vol. 16, 951591, 2022. DOI: 10.3389/fnhum.2022.951591
[2]H. Shin, D. Suma and B. He, “Data from: Closed-loop motor imagery EEG simulation for brain-computer interfaces,” figshare, 2022. DOI: 10.6084/m9.figshare.20383716
from moabb.datasets import Shin2022 dataset = Shin2022() data = dataset.get_data(subjects=[1]) print(data[1])
Dataset summary
#Subj
10
#Chan
16
#Classes
2
#Trials / class
132
Trials length
3 s
Freq
250 Hz
#Sessions
1
#Runs
11
Total_trials
2640
Participants
Population: healthy
Equipment
Amplifier: g.tec g.Nautilus RESEARCH (16 channel)
Electrodes: dry
Montage: standard_1005
Reference: unknown
Preprocessing
Data state: continuous
Data Access
DOI: 10.3389/fnhum.2022.951591
Repository: Figshare
Experimental Protocol
Paradigm: imagery
Feedback: visual
Stimulus: visual cursor
Notes
The BCI2000 headers store no electrode labels, so the channels are named
EEG1..``EEG16`` and no montage is applied; the online classifier usesEEG7/EEG9as the C3 / C4 control pair. Signals are converted from microvolts to volts.- __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]#
Download the archive (once), extract this subject’s LIVE
.datruns.Returns the LIVE session directory holding the
BW/CV/NTrun folders.
- download(subject_list=None, path=None, force_update=False, update_path=None, accept=False, verbose=None)[source]#
Download all data from the dataset.
This function is only useful to download all the dataset at once.
When the dataset declares a
nemar_idand the download provider is not"upstream", the files come from NEMAR’ssourcedata/– the original pre-BIDS distribution, byte-identical to what the upstream host serves. On any NEMAR failure this falls back to the dataset’s own downloader with a warning (unless the provider is pinned to"nemar"). Seesourcedata_path()andmoabb.set_download_provider().- Parameters:
subject_list (list of int | None) – List of subjects id to download, if None all subjects are downloaded. On the NEMAR path each subject is resolved through the deposit’s
sourcedata_provenance.json; deposits enriched before that manifest recorded subjects fetch the whole tree.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) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted. Not used on the NEMAR path.
accept (bool) – Accept licence term to download the data, if any. Default: False. Only relevant to the dataset’s own downloader; NEMAR mirrors are already public.
verbose (bool, str, int, or None) – If not None, override default verbose level (see
mne.verbose()).
- get_additional_metadata(subject: str, session: str, run: str)[source]#
Load additional metadata for a specific subject, session, and run.
This method is intended to be overridden by subclasses to provide additional metadata specific to the dataset. The metadata is typically loaded from an events.tsv file or similar data source.
- 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/.