moabb.datasets.Kaya2018#

class moabb.datasets.Kaya2018(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Kaya2018

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

AuthorsMurat Kaya, Mustafa Kemal Binli, Erkan Ozbay, Hilmi Yanar, Yuriy Mishchenko

🇹🇷 Mersin University, TR·2018
Motor Imagery Code: Kaya2018 7 subjects 1 session 19 ch 200 Hz 3 classes 1.0 s trials CC BY 4.0

Class Labels: left_hand, right_hand, passive

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Overview

Classical motor imagery dataset with left hand, right hand, and rest.

Dataset from

This dataset contains 19-channel EEG recordings from 7 subjects (labeled A-F and J in the original data, mapped to integers 1-7 here) performing a classical (CLA) motor imagery task. Three mental states are cued:

  • - left_hand (code 1): left hand motor imagery
  • right_hand (code 2): right hand motor imagery
  • passive (code 3): passive/rest state

EEG was recorded at 200 Hz with a Nihon Kohden EEG-1200 system using 19 standard 10-20 electrodes plus A1/A2 reference/ground and an X3 sync channel (22 columns total in the data files). Only the 19 EEG channels are used by this adapter.

Each trial consists of a 1-second visual cue followed by a 1.5-2.5 second inter-trial interval. Subjects have between 1 and 3 recording sessions (CLA files) each.

The full Figshare collection contains 77 articles spanning multiple paradigms (CLA, HaLT, 5F, FREEFORM, NoMT). This adapter uses only the 17 CLA files.

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Citation & Impact

Stimulus Protocol
../_images/Kaya2018.svg

1s task window per trial · 3-class motor imagery paradigm · 1 runs/session across 1 sessions

HED Event Tags
HED tags3/3 events annotated

Source: MOABB BIDS HED annotation mapping.

Sensory-event
3
Agent-action
2
Label
1
left_hand
Sensory-eventAgent-action
right_hand
Sensory-eventAgent-action
passive
Sensory-eventLabel

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 · passive
├─ Sensory-event
└─ Label
Channel Summary
Total channels19
EEG19
Montagestandard_1020
Sampling200 Hz
ReferenceSystem 0V (0.55*(C3+C4))
Notch / line50 Hz

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

Classical motor imagery dataset with left hand, right hand, and rest.

Dataset from [1].

This dataset contains 19-channel EEG recordings from 7 subjects (labeled A-F and J in the original data, mapped to integers 1-7 here) performing a classical (CLA) motor imagery task. Three mental states are cued:

  • left_hand (code 1): left hand motor imagery

  • right_hand (code 2): right hand motor imagery

  • passive (code 3): passive/rest state

EEG was recorded at 200 Hz with a Nihon Kohden EEG-1200 system using 19 standard 10-20 electrodes plus A1/A2 reference/ground and an X3 sync channel (22 columns total in the data files). Only the 19 EEG channels are used by this adapter.

Each trial consists of a 1-second visual cue followed by a 1.5-2.5 second inter-trial interval. Subjects have between 1 and 3 recording sessions (CLA files) each.

Note

Subject 6 (F), session 0 (CLA-SubjectF-150916) contains only left_hand and right_hand events (no passive trials). This was one of the earliest recordings in the study.

Subject 7 (J) data was recorded with an interactive BCI interface and has different signal resolution (0.133 uV vs 0.01 uV for other subjects) and a narrower dynamic range.

The full Figshare collection contains 77 articles spanning multiple paradigms (CLA, HaLT, 5F, FREEFORM, NoMT). This adapter uses only the 17 CLA files.

References

[1]

M. Kaya, M. K. Binli, E. Ozbay, H. Yanar, and Y. Mishchenko, “A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces,” Scientific Data, vol. 5, p. 180211, 2018. DOI: 10.1038/sdata.2018.211

from moabb.datasets import Kaya2018
dataset = Kaya2018()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

7

#Chan

19

#Classes

3

#Trials / class

80

Trials length

1 s

Freq

200 Hz

#Sessions

3

#Runs

1

Total_trials

16126

Participants

  • Population: healthy

Equipment

  • Amplifier: Nihon Kohden EEG-1200

  • Montage: standard_1020

  • Reference: System 0V (0.55*(C3+C4))

Preprocessing

  • Data state: raw

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Task type: left_right_hand

  • Feedback: none

  • Stimulus: visual arrow cue

__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]#

Download and return paths to CLA .mat files for a subject.

Parameters:

subject (int) – Subject number (1-7).

Returns:

Paths to downloaded .mat files, one per session.

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