moabb.datasets.Vagaja2023#
- class moabb.datasets.Vagaja2023(subjects=None, sessions=None, *, return_all_modalities=False)[source]#
Bases:
BaseDataset[source]Dataset Snapshot
Vagaja2023
Motor-imagery EEG plus bipolar EMG, skin temperature and accelerometry from 26 healthy volunteers performing cued left/right-hand motor imagery during embodiment-primed MI-BCI training in virtual reality.
Imagery, 2 classes (left_hand vs right_hand)
Imagery Code: Vagaja2023 26 subjects 1 session 38 ch (32 EEG) 500 Hz 2 classes 10.0 s trials CC BY 4.0Class Labels: left_hand, right_hand
Citation & Impact
- Paper DOI10.5281/zenodo.8086086
- CitationsLoading…
- Public APICrossref | OpenAlex
Stimulus Protocol
10s task window per trial · 2-class imagery paradigm · 1 runs/session across 1 sessions
HED Event TagsHED tagsSource: MOABB BIDS HED annotation mapping.
left_handSensory-eventAgent-actionright_handSensory-eventAgent-actionHED tree view
Tree · left_hand
├─ Sensory-event │ ├─ Experimental-stimulus │ └─ Visual-presentation └─ Agent-action └─ Imagine ├─ Move └─ Left └─ HandTree · right_hand
├─ Sensory-event │ ├─ Experimental-stimulus │ └─ Visual-presentation └─ Agent-action └─ Imagine ├─ Move └─ Right └─ HandChannel SummaryTotal channels38EEG32 (active Ag/AgCl)MISC4EMG2Montagestandard_1020Sampling500 HzNotch / line50 HzThis diagram is automatically generated from MOABB metadata. Please consult the original publication to confirm the experimental protocol details.
Motor-imagery EEG during embodiment-primed MI-BCI training in VR [1].
Dataset description
Twenty-six healthy volunteers (10 males, mean age 25.4 +/- 7.4; 16 females, mean age 23 +/- 3.2; all right-handed; three with prior BCI experience) performed cue-based left- vs right-hand motor imagery in an immersive virtual-reality environment (Oculus Rift CV1) during a single lab session of about 90-120 minutes (setup, 4 min resting state, 5 min embodiment induction/breaking, 15 min MI training). A between-subject design tested whether a virtual embodiment (virtual-hand-illusion) priming phase changes the subsequent motor-imagery training: participants were randomly assigned to an
Embodiedgroup (N=13, embodiment induced) or aControlgroup (N=13, embodiment broken). EEG was recorded with a 32-channel LiveAmp system (actiCAP active electrodes, Brain Products GmbH) at 500 Hz, together with bipolar EMG from both forearms, skin temperature (through the BIP2AUX adapter) and a 3-axis accelerometer.Each recorded subject contributes three BrainVision runs – a resting-state baseline, the embodiment (virtual-hand-illusion) phase and the MI training phase – but only the MI run carries left-/right-hand class markers, so this loader exposes that run alone as a single MOABB session. Each trial is cue-locked: the class marker (S 7 left / S 8 right) starts the imagery period and the end-of-trial marker follows 10 s later (continuous feedback begins 1.25 s after the cue), so the exposed interval spans that 10 s window. Every subject provides 20 left-hand and 20 right-hand trials (40 in total).
Subjects 3, 5-10, 12, 14-16, 29 and 31 are Embodied; 17-28 and 30 Control.
By default only the 32 EEG channels are returned; pass
return_all_modalities=Trueto also keep the EMG, temperature and accelerometer channels.References
[1]Vagaja, K., and Vourvopoulos, A. (2023). Electrophysiological Signals of Embodiment and MI-BCI Training in VR. Zenodo. DOI: https://doi.org/10.5281/zenodo.8086086
from moabb.datasets import Vagaja2023 dataset = Vagaja2023() data = dataset.get_data(subjects=[3]) print(data[3])
Dataset summary
#Subj
26
#Chan
32
#Classes
2
#Trials / class
20
Trials length
10 s
Freq
500 Hz
#Sessions
1
#Runs
1
Total_trials
1040
Participants
Population: healthy
Handedness: {‘right’: 26}
BCI experience: mixed (3 of 26 with prior BCI experience)
Equipment
Amplifier: LiveAmp 32 (Brain Products GmbH)
Electrodes: active Ag/AgCl
Montage: standard_1020
Preprocessing
Data state: raw
Data Access
DOI: 10.5281/zenodo.8086086
Data URL: https://zenodo.org/records/8086086
Repository: Zenodo
Experimental Protocol
Paradigm: imagery
Feedback: visual
Stimulus: visual
Notes
Added in version 1.8.
- __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]#
Return
[subject_dir], downloading/extracting GROUPS.zip if needed.
- 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/.