moabb.datasets.BCIComp2020IS#

class moabb.datasets.BCIComp2020IS(subjects=None, sessions=None)[source]#

Bases: BaseDataset

Dataset Snapshot

BCIComp2020IS

BCI Competition 2020 Track 3: Imagined speech classification with 5 phrases using 64-channel EEG. Best competition accuracy 82.6%. IRB: KUIRB-2019-0143-01.

Imagery, 5 classes (Hello vs Helpme vs Stop vs Thankyou vs Yes)

AuthorsJi-Hoon Jeong, Jeong-Hyun Cho, Young-Eun Lee, Seo-Hyun Lee, Gi-Hwan Shin, Young-Seok Kweon, Jose del R. Millan, Klaus-Robert Mueller, Seong-Whan Lee

🇰🇷 Korea University, KR·2022·bcicompetition2020@gmail.com

Imagery Code: BCIComp2020IS 15 subjects 1 session 64 ch 256 Hz 5 classes 3.1 s trials CC BY 4.0

Class Labels: Hello, Helpme, Stop, Thankyou, Yes

Read Paper Compare Similar Report Issue

Overview

BCI Competition 2020 Track 3 - Imagined Speech Classification.

Dataset from the 2020 International BCI Competition

Dataset Description

Fifteen subjects (aged 20-30) performed imagined speech of five phrases: "Hello", "Help me", "Stop", "Thank you", "Yes". EEG was recorded at 1000 Hz using 64 channels in a 10-20 configuration with a BrainAmp amplifier (BrainProducts GmbH), FCz reference, Fpz ground. Data is stored at the native epoch sampling rate of 256 Hz.

Each trial begins with an auditory cue (one of the five words), followed by 4 repetitions of: fixation cross (0.8-1.2 s jittered) then 2 s imagined speech. A 3 s relaxation phase separates blocks. Epochs span -500 ms to 2600 ms relative to cue onset.

Each subject has 300 training trials (60 per class) and 50 validation trials (10 per class). Test trials (50 per subject) have no labels (competition holdout) and are not loaded. Best competition result was 82.6% accuracy.

Open in Overview tab →

Citation & Impact

Paper DOI10.3389/fnhum.2022.898300
CitationsLoading…
Public APICrossref | OpenAlex

HED Event Tags

HED tags5/5 events annotated

Source: MOABB BIDS HED annotation mapping.

Agent-action

5

Sensory-event

5

Hello

Sensory-eventAgent-action

Helpme

Sensory-eventAgent-action

Stop

Sensory-eventAgent-action

Thankyou

Sensory-eventAgent-action

Yes

Sensory-eventAgent-action

HED tree view

Tree · Hello

├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Auditory-presentation
│  └─ Hear
│     └─ Word
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Word
         └─ Label

Tree · Helpme

├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Auditory-presentation
│  └─ Hear
│     └─ Word
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Word
         └─ Label

Tree · Stop

├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Auditory-presentation
│  └─ Hear
│     └─ Word
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Word
         └─ Label

Tree · Thankyou

├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Auditory-presentation
│  └─ Hear
│     └─ Word
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Word
         └─ Label

Tree · Yes

├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Auditory-presentation
│  └─ Hear
│     └─ Word
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Word
         └─ Label

Channel Summary

Total channels64

EEG64

Montage10-05

Sampling256 Hz

ReferenceFCz

Notch / line50 Hz

Overview

BCI Competition 2020 Track 3 - Imagined Speech Classification.

Dataset from the 2020 International BCI Competition [1].

Dataset Description

Fifteen subjects (aged 20-30) performed imagined speech of five phrases: “Hello”, “Help me”, “Stop”, “Thank you”, “Yes”. EEG was recorded at 1000 Hz using 64 channels in a 10-20 configuration with a BrainAmp amplifier (BrainProducts GmbH), FCz reference, Fpz ground. Data is stored at the native epoch sampling rate of 256 Hz.

Each trial begins with an auditory cue (one of the five words), followed by 4 repetitions of: fixation cross (0.8-1.2 s jittered) then 2 s imagined speech. A 3 s relaxation phase separates blocks. Epochs span -500 ms to 2600 ms relative to cue onset.

Each subject has 300 training trials (60 per class) and 50 validation trials (10 per class). Test trials (50 per subject) have no labels (competition holdout) and are not loaded. Best competition result was 82.6% accuracy.

BCI Competition 2020 Track 3 trial structure — Rest, auditory cue, 4 imagined-speech repetitions. — Figure 7 of [1] (CC-BY-4.0). Epoch window: -0.5 to +2.6 s.#

References

[1] (1,2)

Jeong, J.-H. et al. (2022). 2020 International brain-computer interface competition: A review. Frontiers in Human Neuroscience, 16, 898300. https://doi.org/10.3389/fnhum.2022.898300

Code Examples

from moabb.datasets import BCIComp2020IS
dataset = BCIComp2020IS()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	15
#Chan	64
#Classes	5
#Trials / class	80
Trials length	3.1 s
Freq	256 Hz
#Sessions	1
#Runs	3
Total_trials	6000

Participants

Population: healthy

Equipment

Amplifier: BrainAmp (BrainProducts GmbH)
Montage: standard_1005
Reference: FCz

Preprocessing

Data state: epoched

Data Access

DOI: 10.3389/fnhum.2022.898300
Data URL: https://osf.io/pq7vb/
Repository: OSF

Experimental Protocol

Paradigm: imagery
Stimulus: auditory cue

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

Notes

Use CacheConfig to configure caching for get_data(). Use moabb.datasets.bids_interface.get_bids_root to get the BIDS root path.

Added in version 1.5.

data_path(subject, path=None, force_update=False, update_path=None, verbose=None, *, split=None)[source]#

Return the local path to a subject file.

Downloads all (training + validation) files for subject via moabb.datasets.download.data_dl() if they are not already present. Returns the path for the requested split if one is provided, otherwise the training file path.

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)[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 | pandas.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

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 (sklearn.pipeline.Pipeline | None) – Optional processing pipeline to apply to the data. To generate an adequate pipeline, we recommend using moabb.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[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