moabb.datasets.Weibo2014

class moabb.datasets.Weibo2014(subjects=None, sessions=None, *, return_all_modalities=False)

Bases: BaseDataset

[source]

Dataset Snapshot

Weibo2014

Motor Imagery, 7 classes

AuthorsWeibo Yi, Shuang Qiu, Kun Wang, Hongzhi Qi, Lixin Zhang, Peng Zhou, Feng He, Dong Ming

🇨🇳 Tianjin University, CN·2014·qhz@tju.edu.cn

Motor Imagery Code: Weibo2014 10 subjects 1 session 60 ch 200 Hz 7 classes 8.0 s trials CC0 1.0

Class Labels: left_hand, right_hand, hands, feet, left_hand_right_foot, right_hand_left_foot, rest

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Overview

Motor Imagery dataset from Weibo et al 2014.

Dataset from the article Evaluation of EEG oscillatory patterns and cognitive process during simple and compound limb motor imagery

It contains data recorded on 10 subjects, with 60 electrodes.

This dataset was used to investigate the differences of the EEG patterns between simple limb motor imagery and compound limb motor imagery. Seven kinds of mental tasks have been designed, involving three tasks of simple limb motor imagery (left hand, right hand, feet), three tasks of compound limb motor imagery combining hand with hand/foot (both hands, left hand combined with right foot, right hand combined with left foot) and rest state.

At the beginning of each trial (8 seconds), a white circle appeared at the center of the monitor. After 2 seconds, a red circle (preparation cue) appeared for 1 second to remind the subjects of paying attention to the character indication next. Then red circle disappeared and character indication (‘Left Hand’, ‘Left Hand & Right Foot’, et al) was presented on the screen for 4 seconds, during which the participants were asked to perform kinesthetic motor imagery rather than a visual type of imagery while avoiding any muscle movement. After 7 seconds, ‘Rest’ was presented for 1 second before next trial (Fig. 1(a)). The experiments were divided into 9 sections, involving 8 sections consisting of 60 trials each for six kinds of MI tasks (10 trials for each MI task in one section) and one section consisting of 80 trials for rest state. The sequence of six MI tasks was randomized. Intersection break was about 5 to 10 minutes.

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Benchmark Context

WithinSession

Included in 3 MOABB benchmark table(s). Scores are across available pipelines (WithinSession accuracy).

Sample frame: 10 subjects × 1 sessions

• MI left vs right 19 pipelinesMax 85.29 · Median 78.41 · Mean 74.74 · Std 9.12
• MI all classes 16 pipelinesMax 63.89 · Median 41.77 · Mean 43.27 · Std 15.19
• MI right hand vs feet 16 pipelinesMax 93.25 · Median 88.52 · Mean 81.74 · Std 12.17

Citation & Impact

• Paper DOI10.1371/journal.pone.0114853
• CitationsLoading…
• Public APICrossref | OpenAlex
• MOABB tables3 (WithinSession)
• Page Views
  30d: 59 · all-time: 891

  #11 of 151 · Top 8% most viewed

  Updated: 2026-03-20 UTC

Stimulus Protocol

8s task window per trial · 7-class motor imagery paradigm · 1 runs/session across 1 sessions

HED Event Tags

HED tags7/7 events annotated

Source: MOABB BIDS HED annotation mapping.

Sensory-event

7

Agent-action

6

Experimental-stimulus

1

Rest

1

Visual-presentation

1

left_hand

Sensory-eventAgent-action

right_hand

Sensory-eventAgent-action

hands

Sensory-eventAgent-action

feet

Sensory-eventAgent-action

left_hand_right_foot

Sensory-eventAgent-action

right_hand_left_foot

Sensory-eventAgent-action

rest

Sensory-eventExperimental-stimulusVisual-presentationRest

HED tree view

Tree · left_hand

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

Tree · right_hand

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

Tree · hands

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

Tree · feet

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Foot

Tree · left_hand_right_foot

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   ├─ Imagine
   │  ├─ Move
   │  └─ Left
   │     └─ Hand
   └─ Imagine
      ├─ Move
      └─ Right
         └─ Foot

Tree · right_hand_left_foot

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   ├─ Imagine
   │  ├─ Move
   │  └─ Right
   │     └─ Hand
   └─ Imagine
      ├─ Move
      └─ Left
         └─ Foot

Tree · rest

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

Channel Summary

Total channels60

EEG60 (Ag/AgCl)

EOG2

MISC2

Montage10-05

Sampling200 Hz

Referencenose

Filter{'bandpass': [0.5, 100], 'notch_hz': 50}

Notch / line50 Hz

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

Overview

Motor Imagery dataset from Weibo et al 2014.

Dataset from the article Evaluation of EEG oscillatory patterns and cognitive process during simple and compound limb motor imagery [1].

It contains data recorded on 10 subjects, with 60 electrodes.

This dataset was used to investigate the differences of the EEG patterns between simple limb motor imagery and compound limb motor imagery. Seven kinds of mental tasks have been designed, involving three tasks of simple limb motor imagery (left hand, right hand, feet), three tasks of compound limb motor imagery combining hand with hand/foot (both hands, left hand combined with right foot, right hand combined with left foot) and rest state.

At the beginning of each trial (8 seconds), a white circle appeared at the center of the monitor. After 2 seconds, a red circle (preparation cue) appeared for 1 second to remind the subjects of paying attention to the character indication next. Then red circle disappeared and character indication (‘Left Hand’, ‘Left Hand & Right Foot’, et al) was presented on the screen for 4 seconds, during which the participants were asked to perform kinesthetic motor imagery rather than a visual type of imagery while avoiding any muscle movement. After 7 seconds, ‘Rest’ was presented for 1 second before next trial (Fig. 1(a)). The experiments were divided into 9 sections, involving 8 sections consisting of 60 trials each for six kinds of MI tasks (10 trials for each MI task in one section) and one section consisting of 80 trials for rest state. The sequence of six MI tasks was randomized. Intersection break was about 5 to 10 minutes.

References

[1]

Yi, Weibo, et al. “Evaluation of EEG oscillatory patterns and cognitive process during simple and compound limb motor imagery.” PloS one 9.12 (2014). https://doi.org/10.1371/journal.pone.0114853

Code Examples

from moabb.datasets import Weibo2014
dataset = Weibo2014()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	10
#Chan	60
#Classes	7
#Trials / class	80
Trials length	4 s
Freq	200 Hz
#Sessions	1
#Runs	1
Total_trials	5600

Participants

• Population: healthy

• Age: 24 (range: 23-25) years

• Handedness: right-handed

• BCI experience: naive

Equipment

• Amplifier: Neuroscan SynAmps2

• Electrodes: Ag/AgCl

• Montage: standard_1005

• Reference: nose

Preprocessing

• Data state: preprocessed

• Bandpass filter: 0.5-50 Hz

• Steps: bandpass filtering, downsampling

• Re-reference: nose

Data Access

• DOI: 10.1371/journal.pone.0114853

• Data URL: http://dx.doi.org/10.7910/DVN/27306

• Repository: Harvard Dataverse Database

Experimental Protocol

• Paradigm: imagery

• Feedback: none

• Stimulus: text cues

__init__(subjects=None, sessions=None, *, return_all_modalities=False)

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)

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.

• generate_figures (bool) – If True, generate interactive neural signature HTML figures in {bids_root}/derivatives/neural_signatures/. Requires plotly (pip install moabb[interactive]). Default is False.

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)

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)

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

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)

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)

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

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