moabb.datasets.Yi2025#

class moabb.datasets.Yi2025(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Yi2025

Motor Imagery, 8 classes

AuthorsWeibo Yi, Jiaming Chen, Dan Wang, Xinkang Hu, Meng Xu, Fangda Li, Shuhan Wu, Jin Qian

🇨🇳 Beijing University of Technology, CN·2025
Motor Imagery Code: Yi2025 18 subjects 1 session 62 ch 1000 Hz 8 classes 4.0 s trials CC BY-NC-ND 4.0

Class Labels: hand_open_close, wrist_flex_ext, wrist_abd_add, elbow_pron_sup, elbow_flex_ext, shoulder_pron_sup, shoulder_abd_add, shoulder_flex_ext

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Overview

Multi-joint upper-limb MI dataset from Yi et al. 2025.

Dataset from

This dataset contains EEG recordings from 18 healthy subjects performing 8-class motor imagery of different upper-limb joints. Recorded with 64-channel Neuroscan SynAmps2 at 1000 Hz.

The 8 MI classes correspond to multi-joint movements:

1. Hand open/close 2. Wrist flexion/extension 3. Wrist abduction/adduction 4. Elbow pronation/supination 5. Elbow flexion/extension 6. Shoulder pronation/supination 7. Shoulder abduction/adduction 8. Shoulder flexion/extension

Each subject performed 1 session with 8 blocks of 40 trials (5 per class), for 320 total trials. Trial structure: 2 s fixation, 2 s cue, 4 s MI, 10-12 s rest.

Raw Neuroscan .cnt files are loaded from the FineMI.zip archive on Figshare (~14.2 GB). Auxiliary channels (M1, M2, HEO, VEO, EKG, EMG) are dropped, leaving 62 EEG channels. Each block is loaded as a separate run.

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Citation & Impact

Stimulus Protocol
../_images/Yi2025.svg

4s task window per trial · 8-class motor imagery paradigm · 8 runs/session across 1 sessions

HED Event Tags
HED tags8/8 events annotated

Source: MOABB BIDS HED annotation mapping.

Label
8
Sensory-event
8
hand_open_close
Sensory-eventLabel
wrist_flex_ext
Sensory-eventLabel
wrist_abd_add
Sensory-eventLabel
elbow_pron_sup
Sensory-eventLabel
elbow_flex_ext
Sensory-eventLabel
shoulder_pron_sup
Sensory-eventLabel
shoulder_abd_add
Sensory-eventLabel
shoulder_flex_ext
Sensory-eventLabel

HED tree view

Tree · hand_open_close
├─ Sensory-event
└─ Label
Tree · wrist_flex_ext
├─ Sensory-event
└─ Label
Tree · wrist_abd_add
├─ Sensory-event
└─ Label
Tree · elbow_pron_sup
├─ Sensory-event
└─ Label
Tree · elbow_flex_ext
├─ Sensory-event
└─ Label
Tree · shoulder_pron_sup
├─ Sensory-event
└─ Label
Tree · shoulder_abd_add
├─ Sensory-event
└─ Label
Tree · shoulder_flex_ext
├─ Sensory-event
└─ Label
Channel Summary
Total channels62
EEG62
Montage10-05
Sampling1000 Hz
Referenceleft mastoid (M1)
Notch / line50 Hz

This diagram is automatically generated from MOABB metadata. Please consult the original publication to confirm the experimental protocol details.

Multi-joint upper-limb MI dataset from Yi et al. 2025.

Dataset from [1].

This dataset contains EEG recordings from 18 healthy subjects performing 8-class motor imagery of different upper-limb joints. Recorded with 64-channel Neuroscan SynAmps2 at 1000 Hz.

The 8 MI classes correspond to multi-joint movements:

  1. Hand open/close

  2. Wrist flexion/extension

  3. Wrist abduction/adduction

  4. Elbow pronation/supination

  5. Elbow flexion/extension

  6. Shoulder pronation/supination

  7. Shoulder abduction/adduction

  8. Shoulder flexion/extension

Each subject performed 1 session with 8 blocks of 40 trials (5 per class), for 320 total trials. Trial structure: 2 s fixation, 2 s cue, 4 s MI, 10-12 s rest.

Raw Neuroscan .cnt files are loaded from the FineMI.zip archive on Figshare (~14.2 GB). Auxiliary channels (M1, M2, HEO, VEO, EKG, EMG) are dropped, leaving 62 EEG channels. Each block is loaded as a separate run.

Note

The first download requires the full FineMI.zip (14.2 GB).

References

[1]

Yi, W., Chen, J., Wang, D., et al. (2025). A multi-modal dataset of EEG and fNIRS for motor imagery of multi-types of joints from unilateral upper limb. Scientific Data, 12, 953. https://doi.org/10.1038/s41597-025-05286-0

from moabb.datasets import Yi2025
dataset = Yi2025()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

18

#Chan

62

#Classes

8

#Trials / class

40

Trials length

4 s

Freq

250 Hz

#Sessions

1

#Runs

1

Total_trials

5760

Participants

  • Population: healthy

  • Handedness: right

  • BCI experience: naive

Equipment

  • Amplifier: Neuroscan SynAmps2

  • Montage: standard_1005

  • Reference: left mastoid (M1)

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Feedback: none

  • Stimulus: video + text

Notes

Added in version 1.2.0.

__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)[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. If None the 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 in subject_list are converted.

  • overwrite (bool) – If True, existing BIDS files for a subject are removed before saving. Default is False.

  • 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.

Returns:

bids_root – Path to the root of the written BIDS dataset.

Return type:

pathlib.Path

Examples

>>> from moabb.datasets import AlexMI
>>> dataset = AlexMI()
>>> bids_root = dataset.convert_to_bids(path='/tmp/bids', subjects=[1])

See also

CacheConfig

Cache configuration for get_data().

moabb.datasets.bids_interface.get_bids_root

Return the BIDS root path.

Notes

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)_PATH is 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:

list of str

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.

Parameters:

  • subject_list (list of int | None) – List of subjects id to download, if None all subjects are downloaded.

  • path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is 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.

  • accept (bool) – Accept licence term to download the data, if any. Default: False

  • 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) → None | DataFrame[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:

  • subject (str) – The identifier for the subject.

  • session (str) – The identifier for the session.

  • run (str) – The identifier for the run.

Returns:

A DataFrame containing the additional metadata if available, otherwise None.

Return type:

None | pd.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

BaseDataset.get_data

Parameters:

  • subjects (List of int) – List of subject number

  • block_list (List of int) – List of block number

  • repetition_list (List of int) – List of repetition number inside a block

Returns:

data – dict containing the raw data

Return type:

Dict

get_data(subjects=None, cache_config=None, process_pipeline=None)[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 *_pipeline arguments. These pipelines are applied in the following order: raw_pipeline -> epochs_pipeline -> array_pipeline. If a *_pipeline argument is None, the step will be skipped. Therefore, the array_pipeline may either receive a mne.io.Raw or a mne.Epochs object as input depending on whether epochs_pipeline is None or not.

Parameters:

  • subjects (List of int) – List of subject number

  • cache_config (dict | CacheConfig) – Configuration for caching of datasets. See CacheConfig for details.

  • process_pipeline (Pipeline | None) – Optional processing pipeline to apply to the data. To generate an adequate pipeline, we recommend using moabb.utils.make_process_pipelines(). This pipeline will receive mne.io.BaseRaw objects. The steps names of this pipeline should be elements of StepType. According to their name, the steps should either return a mne.io.BaseRaw, a mne.Epochs, or a numpy.ndarray(). This pipeline must be “fixed” because it will not be trained, i.e. no call to fit will be made.

Returns:

data – dict containing the raw data

Return type:

Dict

property metadata: DatasetMetadata | None[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