moabb.datasets.Kojima2024B#

class moabb.datasets.Kojima2024B(events={'NonTarget': [101, 102, 103, 104], 'Target': [111, 112, 113, 114]}, task='all')[source]#

Class for Kojima2024B dataset management. P300 dataset.

Dataset summary

#Subj

15

#Chan

64

#Trials / class

2160 NT / 720 T

Trials length

1 s

Freq

1000 Hz

#Sessions

1

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.

Recording Details:

  • 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.

Parameters:

  • events (dict) – Event mapping for the dataset.

  • task ({"all", "2stream", "4stream"}, optional) –

    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.

    Note

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

    • "2stream": 1080 NT / 360 T

    • "4stream": 1080 NT / 360 T

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

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

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_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