moabb.datasets.Zhou2020#

class moabb.datasets.Zhou2020(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Zhou2020

Motor Imagery, 4 classes (left_hand vs right_hand vs feet vs rest)

AuthorsQing Zhou, Jiafan Lin, Lin Yao, Yueming Wang, Yan Han, Kedi Xu

🇨🇳 Zhejiang University, CN·2021
Motor Imagery Code: Zhou2020 20 subjects 7 sessions 41 ch 500 Hz 4 classes 5.0 s trials CC BY 4.0

Class Labels: left_hand, right_hand, feet, rest

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Overview

7-day motor imagery BCI EEG dataset from Zhou et al 2021.

Dataset from the article Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects

It contains data recorded on 20 subjects over 7 sessions (one session every ~2 days over 2 weeks) with no feedback training. Two groups of subjects were recorded with a 64-channel Neuroscan SynAmps2 system at 500 Hz:

  • - S-subjects (subjects 1-12): 41 EEG + 4 EOG channels
  • A-subjects (subjects 13-20): 26 EEG + 2 EOG channels

Four MI classes were recorded: left hand, right hand, both feet, and idle/rest. Each session contains ~6 runs of 40 trials each (10 per class), giving ~240 trials per session and ~1680 trials per subject.

The data is stored as Neuroscan NSsignal NPZ files with continuous recordings (band-pass 0.5-100 Hz, 50 Hz notch). Events are encoded using GDF/BioSig codes: 769 (left), 770 (right), 771 (feet), 780 (rest).

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Citation & Impact

Stimulus Protocol
../_images/Zhou2020.svg

5s task window per trial · 4-class motor imagery paradigm · 6 runs/session across 7 sessions

HED Event Tags
HED tags4/4 events annotated

Source: MOABB BIDS HED annotation mapping.

Sensory-event
4
Agent-action
3
Experimental-stimulus
1
Rest
1
Visual-presentation
1
left_hand
Sensory-eventAgent-action
right_hand
Sensory-eventAgent-action
feet
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 · feet
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Foot
Tree · rest
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Rest
Channel Summary
Total channels41
EEG41 (Ag/AgCl)
Montage10-05
Sampling500 Hz
Referencevertex (Cz)
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.

7-day motor imagery BCI EEG dataset from Zhou et al 2021.

Dataset from the article Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects [1].

It contains data recorded on 20 subjects over 7 sessions (one session every ~2 days over 2 weeks) with no feedback training. Two groups of subjects were recorded with a 64-channel Neuroscan SynAmps2 system at 500 Hz:

  • S-subjects (subjects 1-12): 41 EEG + 4 EOG channels

  • A-subjects (subjects 13-20): 26 EEG + 2 EOG channels

Four MI classes were recorded: left hand, right hand, both feet, and idle/rest. Each session contains ~6 runs of 40 trials each (10 per class), giving ~240 trials per session and ~1680 trials per subject.

The data is stored as Neuroscan NSsignal NPZ files with continuous recordings (band-pass 0.5-100 Hz, 50 Hz notch). Events are encoded using GDF/BioSig codes: 769 (left), 770 (right), 771 (feet), 780 (rest).

References

[1]

Zhou, Q., Lin, J., Yao, L., Wang, Y., Han, Y., Xu, K. (2021). Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects. Frontiers in Human Neuroscience, 15, 701091. https://doi.org/10.3389/fnhum.2021.701091

from moabb.datasets import Zhou2020
dataset = Zhou2020()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

20

#Chan

26

#Classes

4

#Trials / class

60

Trials length

5 s

Freq

500 Hz

#Sessions

7

#Runs

6

Total_trials

33600

Participants

  • Population: healthy

  • Age: 23.2 (range: 21-27) years

  • Handedness: right-handed

  • BCI experience: mixed

Equipment

  • Amplifier: Neuroscan SynAmps2

  • Electrodes: Ag/AgCl

  • Montage: standard_1005

  • Reference: vertex (Cz)

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Feedback: none

  • Stimulus: arrow cues

__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

Examples using moabb.datasets.Zhou2020#

Benchmarking with MOABB

Benchmarking with MOABB