moabb.datasets.Zhou2016#

class moabb.datasets.Zhou2016(subjects=None, sessions=None)[source]#

Bases: BaseBIDSDataset

[source]

Dataset Snapshot

Zhou2016

Motor Imagery, 3 classes (left_hand vs right_hand vs feet)

AuthorsBangyan Zhou, Xiaopei Wu, Zhao Lv, Lei Zhang, Xiaojin Guo

🇨🇳 Anhui University, CN·2016·wxp2001@ahu.edu.cn
Motor Imagery Code: Zhou2016 4 subjects 3 sessions 14 ch 250 Hz 3 classes 10.0 s trials CC BY 4.0

Class Labels: left_hand, right_hand, feet

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Overview

Motor Imagery dataset from Zhou et al 2016.

Dataset from the article A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface This dataset contains data recorded on 4 subjects performing 3 type of motor imagery: left hand, right hand and feet.

Every subject went through three sessions, each of which contained two consecutive runs with several minutes inter-run breaks, and each run comprised 75 trials (25 trials per class). The intervals between two sessions varied from several days to several months.

A trial started by a short beep indicating 1 s preparation time, and followed by a red arrow pointing randomly to three directions (left, right, or bottom) lasting for 5 s and then presented a black screen for 4 s. The subject was instructed to immediately perform the imagination tasks of the left hand, right hand or foot movement respectively according to the cue direction, and try to relax during the black screen.

Open in Overview tab →

Benchmark Context

WithinSession

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

Sample frame: 4 subjects × 3 sessions

  • MI left vs right 19 pipelinesMax 95.65 · Median 92.96 · Mean 89.47 · Std 9.10
  • MI all classes 16 pipelinesMax 85.25 · Median 83.01 · Mean 74.74 · Std 15.43
  • MI right hand vs feet 16 pipelinesMax 97.18 · Median 95.08 · Mean 92.02 · Std 8.56

Citation & Impact

Stimulus Protocol
../_images/Zhou2016.svg

10s task window per trial · 3-class motor imagery paradigm · 2 runs/session across 3 sessions

HED Event Tags
HED tags3/3 events annotated

Source: MOABB BIDS HED annotation mapping.

Agent-action
3
Sensory-event
3
left_hand
Sensory-eventAgent-action
right_hand
Sensory-eventAgent-action
feet
Sensory-eventAgent-action

HED tree view

Tree · left_hand
├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Visual-presentation
│  └─ Leftward
│     └─ Arrow
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Left
         └─ Hand
Tree · right_hand
├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Visual-presentation
│  └─ Rightward
│     └─ Arrow
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Right
         └─ Hand
Tree · feet
├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Visual-presentation
│  └─ Downward
│     └─ Arrow
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Foot
Channel Summary
Total channels14
EEG14 (EEG)
Montagestandard_1020
Sampling250 Hz
Referenceleft mastoid
Filter0.1-100 Hz bandpass, 50 Hz notch
Notch / line50 Hz

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

Motor Imagery dataset from Zhou et al 2016.

Dataset from the article A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface [1]. This dataset contains data recorded on 4 subjects performing 3 type of motor imagery: left hand, right hand and feet.

Every subject went through three sessions, each of which contained two consecutive runs with several minutes inter-run breaks, and each run comprised 75 trials (25 trials per class). The intervals between two sessions varied from several days to several months.

A trial started by a short beep indicating 1 s preparation time, and followed by a red arrow pointing randomly to three directions (left, right, or bottom) lasting for 5 s and then presented a black screen for 4 s. The subject was instructed to immediately perform the imagination tasks of the left hand, right hand or foot movement respectively according to the cue direction, and try to relax during the black screen.

References

[1]

Zhou B, Wu X, Lv Z, Zhang L, Guo X (2016) A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface. PLoS ONE 11(9). https://doi.org/10.1371/journal.pone.0162657

from moabb.datasets import Zhou2016
dataset = Zhou2016()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

4

#Chan

14

#Classes

3

#Trials / class

160

Trials length

5 s

Freq

250 Hz

#Sessions

3

#Runs

2

Total_trials

11496

Participants

  • Population: healthy

  • BCI experience: experienced

Equipment

  • Electrodes: EEG

  • Montage: standard_1020

  • Reference: left mastoid

Preprocessing

  • Data state: raw

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Feedback: none

  • Stimulus: visual arrow and beep

__init__(subjects=None, sessions=None)[source]#

Initialize the BIDS dataset.

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. 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)[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.

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

get_metainfo(path=None)[source]#

Fetch a Zenodo record by its ID.

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

Examples using moabb.datasets.Zhou2016#

Tutorial 2: Using multiple datasets

Tutorial 2: Using multiple datasets

Tutorial 3: Benchmarking multiple pipelines

Tutorial 3: Benchmarking multiple pipelines

Tutorial 4: Creating custom datasets

Tutorial 4: Creating custom datasets

Cross-Session on Multiple Datasets

Cross-Session on Multiple Datasets

Cache on disk intermediate data processing states

Cache on disk intermediate data processing states

Fixed interval windows processing

Fixed interval windows processing

Benchmarking with MOABB showing the CO2 footprint

Benchmarking with MOABB showing the CO2 footprint

Select Electrodes and Resampling

Select Electrodes and Resampling