moabb.datasets.Kojima2024B#

class moabb.datasets.Kojima2024B(events={'NonTarget': [101, 102, 103, 104], 'Target': [111, 112, 113, 114]}, task='all', subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Kojima2024B

Four-class ASME BCI investigation comparing two strategies for multiclassing: ASME-4stream (four streams with single target stimulus each) vs ASME-2stream (two streams with two target stimuli each)

P300 / ERP, 2 classes (Target vs NonTarget)

AuthorsSimon Kojima, Shin'ichiro Kanoh

🇯🇵 Shibaura Institute of Technology, JP·2024·simon.kojima@ieee.org
P300 / ERP Code: Kojima2024B 15 subjects 1 session 64 ch 1000 Hz 2 classes 90.0 s trials CC0 1.0

Class Labels: Target, NonTarget

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Overview

Class for Kojima2024B dataset management. P300 dataset.

Dataset description

This dataset originates from a study investigating a four-class auditory BCI based on auditory stream segregation (ASME-BCI)

In the experiment, participants focused on auditory streams, leveraging auditory stream segregation to selectively attend to stimuli in the target stream. The dataset includes both 2-stream and 4-stream conditions:

4-stream condition: Participants focused on one of four auditory streams. Each stream contained a two-stimulus oddball sequence composed of one deviant stimulus and one standard stimulus.

The sequence below illustrates an example trial. For instance, when D3 is the target stimulus, the participant attended to Stream3 and selectively listened for D3. In this case, D3 is the target, and D1, D2, and D4 are considered non-target stimuli.

2-stream condition: Participants focused on one of two auditory streams. Each stream contained a three-stimulus oddball sequence composed of two deviant stimuli and one standard stimulus.

The sequence below illustrates an example trial. For instance, when D4 is the target stimulus, the participant attended to Stream2 and selectively listened for D4. In this case, D4 is the target, and D1, D2, and D3 are considered non-target stimuli.

Each participant completed 1 session consisting of 6 runs. Each run included 4 trials, each with a different target stimulus. In each trial, all deviant stimuli (D1--D4) were presented 15 times.

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Citation & Impact

Stimulus Protocol
../_images/Kojima2024B.svg

90s task window per trial · 2-class p300 / erp paradigm · 12 runs/session across 1 sessions

HED Event Tags
HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus
2
Sensory-event
2
Visual-presentation
2
Non-target
1
Target
1
Target
Sensory-eventExperimental-stimulusVisual-presentationTarget
NonTarget
Sensory-eventExperimental-stimulusVisual-presentationNon-target

HED tree view

Tree · Target
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Target
Tree · NonTarget
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Non-target
Channel Summary
Total channels64
EEG64 (EEG)
EOG2
Montagestandard_1020
Sampling1000 Hz
Referenceright mastoid
Notch / line50 Hz

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

Class for Kojima2024B dataset management. P300 dataset.

Dataset description

This dataset [1] originates from a study investigating a four-class auditory BCI based on auditory stream segregation (ASME-BCI) [2].

In the experiment, participants focused on auditory streams, leveraging auditory stream segregation to selectively attend to stimuli in the target stream. The dataset includes both 2-stream and 4-stream conditions:

4-stream condition: Participants focused on one of four auditory streams. Each stream contained a two-stimulus oddball sequence composed of one deviant stimulus and one standard stimulus.

The sequence below illustrates an example trial. For instance, when D3 is the target stimulus, the participant attended to Stream3 and selectively listened for D3. In this case, D3 is the target, and D1, D2, and D4 are considered non-target stimuli.

Stream4  -------- S4 -------- S4 -------- D4 -------- S4 -------- S4 --
Stream3  ----- S3 -------- S3 -------- S3 -------- D3 -------- S3 -----
Stream2  -- S2 -------- S2 -------- D2 -------- S2 -------- S2 --------
Stream1  S1 -------- D1 -------- S1 -------- S1 -------- S1 -----------

2-stream condition: Participants focused on one of two auditory streams. Each stream contained a three-stimulus oddball sequence composed of two deviant stimuli and one standard stimulus.

The sequence below illustrates an example trial. For instance, when D4 is the target stimulus, the participant attended to Stream2 and selectively listened for D4. In this case, D4 is the target, and D1, D2, and D3 are considered non-target stimuli.

Stream2  -- S2 --- D3 --- S2 --- S2 --- S2 --- S2 --- D4 ---
Stream1  S1 --- S1 --- D1 --- S1 --- S1 --- D2 --- S1 --- S1

Each participant completed 1 session consisting of 6 runs. Each run included 4 trials, each with a different target stimulus. In each trial, all deviant stimuli (D1–D4) were presented 15 times.

References

[1]

Kojima, S. (2024). Replication Data for: Four-class ASME BCI: investigation of the feasibility and comparison of two strategies for multiclassing. Harvard Dataverse, V1. DOI: https://doi.org/10.7910/DVN/1UJDV6

[2]

Kojima, S. & Kanoh, S. (2024). Four-class ASME BCI: investigation of the feasibility and comparison of two strategies for multiclassing. Frontiers in Human Neuroscience 18:1461960. DOI: https://doi.org/10.3389/fnhum.2024.1461960

from moabb.datasets import Kojima2024B
dataset = Kojima2024B()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

15

#Chan

64

#Trials / class

2160 NT / 720 T

Trials length

1 s

Freq

1000 Hz

#Sessions

1

Participants

  • Population: healthy

  • Age: 22.8 (range: 21-24) years

Equipment

  • Amplifier: BrainAmp

  • Electrodes: EEG

  • Montage: standard_1020

  • Reference: right mastoid

Preprocessing

  • Data state: raw

Data Access

Experimental Protocol

  • Paradigm: p300

  • Task type: auditory stream segregation with oddball

  • Tasks: ASME-4stream, ASME-2stream

  • Feedback: none

  • Stimulus: auditory tones

Notes

  • EEG signals were recorded using a BrainAmp system (Brain Products, Germany) at a sampling rate of 1000 Hz.

    • Data were collected in Tokyo, Japan, where the power line frequency is 50 Hz.

    • EEG was recorded from 64 scalp electrodes according to the international 10–20 system: Fp1, Fp2, AF7, AF3, AFz, AF4, AF8, F7, F5, F3, F1, Fz, F2, F4, F6, F8, FT9, FT7, FC5, FC3, FC1, FCz, FC2, FC4, FC6, FT8, FT10, T7, C5, C3, C1, Cz, C2, C4, C6, T8, TP9, TP7, CP5, CP3, CP1, CPz, CP2, CP4, CP6, TP8, TP10, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO7, PO3, POz, PO4, PO8, O1, Oz, O2

      EEG signals were referenced to the right mastoid and grounded to the left mastoid.

    • EOG was recorded using 2 electrodes (vEOG and hEOG), placed above/below and lateral to one eye.

param events:

Event mapping for the dataset.

type events:

dict

param task:

Which task condition to include:

  • "all": load both 2-stream and 4-stream conditions (default).

  • "2stream": load only the 2-stream condition.

  • "4stream": load only the 4-stream condition.

For each task condition, the total number of trials per class is:

  • "2stream": 1080 NT / 360 T

  • "4stream": 1080 NT / 360 T

type task:

{“all”, “2stream”, “4stream”}, optional

__init__(events={'NonTarget': [101, 102, 103, 104], 'Target': [111, 112, 113, 114]}, task='all', 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_subject_to_subject_id(subjects)[source]#

Convert subject number(s) to subject ID(s). (In this dataset, subject IDs are encoded using alphabet letters.)

Parameters:

subjects (int or list of int) – Subject number(s) to convert.

Returns:

subject_id – Converted subject ID(s).

Return type:

str or list of str

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)[source]#

Return the data paths of a single subject.

Parameters:

  • subject (int) – The subject number to fetch data for.

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

Returns:

A list containing the Path object for the subject’s data file.

Return type:

list

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()).

download_by_subject(subject, path=None)[source]#

Download and extract the dataset.

Parameters:

  • subject (int) – The subject number to download the dataset for.

  • path (str | None) – The path to the directory where the dataset should be downloaded. If None, the default directory is used.

Returns:

path – The dataset path.

Return type:

str

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