moabb.datasets.BI2013a#

class moabb.datasets.BI2013a(non_adaptive=True, adaptive=False, training=True, online=False, subjects=None, sessions=None, *, return_all_modalities=False, **kwargs)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

BI2013a

EEG recordings of 24 subjects doing a visual P300 Brain-Computer Interface experiment comparing adaptive vs non-adaptive calibration using Riemannian geometry

P300 / ERP, 2 classes (Target vs NonTarget)

AuthorsE. Vaineau, A. Barachant, A. Andreev, P. Rodrigues, G. Cattan, M. Congedo

🇫🇷 GIPSA-lab, CNRS, University Grenoble-Alpes, Grenoble INP, FR·2019
P300 / ERP Code: BrainInvaders2013a 24 subjects 8 sessions 16 ch 512 Hz 2 classes 1.0 s trials CC BY 1.0

Class Labels: Target, NonTarget

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Overview

P300 dataset BI2013a from a "Brain Invaders" experiment.

Dataset following the setup from carried-out at University of Grenoble Alpes.

This dataset concerns an experiment carried out at GIPSA-lab (University of Grenoble Alpes, CNRS, Grenoble-INP) in 2013. The recordings concerned 24 subjects in total. Subjects 1 to 7 participated to eight sessions, run in different days, subject 8 to 24 participated to one session. Each session consisted in two runs, one in a Non-Adaptive (classical) and one in an Adaptive (calibration-less) mode of operation. The order of the runs was randomized for each session. In both runs there was a Training (calibration) phase and an Online phase, always passed in this order. In the non-Adaptive run the data from the Training phase was used for classifying the trials on the Online phase using the training-test version of the MDM algorithm In the Adaptive run, the data from the training phase was not used at all, instead the classifier was initialized with generic class geometric means and continuously adapted to the incoming data using the Riemannian method explained in Subjects were completely blind to the mode of operation and the two runs appeared to them identical.

In the Brain Invaders P300 paradigm, a repetition is composed of 12 flashes, of which 2 include the Target symbol (Target flashes) and 10 do not (non-Target flash). Please see for a description of the paradigm. For this experiment, in the Training phases the number of flashes is fixed (80 Target flashes and 400 non-Target flashes). In the Online phases the number of Target and non-Target still are in a ratio 1/5, however their number is variable because the Brain Invaders works with a fixed number of game levels, however the number of repetitions needed to destroy the target (hence to proceed to the next level) depends on the user’s performance In any case, since the classes are unbalanced, an appropriate score must be used for quantifying the performance of classification methods (e.g., balanced accuracy, AUC methods, etc).

Data were acquired with a Nexus (TMSi, The Netherlands) EEG amplifier:

Sampling Frequency: 512 samples per second Digital Filter: no Electrodes: 16 wet Silver/Silver Chloride electrodes positioned at FP1, FP2, F5, AFz, F6, T7, Cz, T8, P7, P3, Pz, P4, P8, O1, Oz, O2 according to the 10/20 international system. Reference: left ear-lobe. * Ground: N/A.

:Principal Investigators: Erwan Vaineau, Dr. Alexandre Barachant :Scientific Supervisor: Dr. Marco Congedo :Technical Supervisor: Anton Andreev

Open in Overview tab →

Benchmark Context

WithinSession

Included in 1 MOABB benchmark table(s). Scores are across available pipelines (WithinSession accuracy).

Sample frame: 24 subjects × 8 sessions

  • ERP/P300 all classes 5 pipelinesMax 92.71 · Median 82.07 · Mean 84.62 · Std 6.90

Citation & Impact

Stimulus Protocol
../_images/BI2013a.svg

1s task window per trial · 2-class p300 / erp paradigm · 2 runs/session across 8 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 channels16
EEG16 (wet Silver/Silver Chloride electrodes)
Montagestandard_1020
Sampling512 Hz
Referenceleft earlobe
Filterno digital filter applied
Notch / line50 Hz

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

P300 dataset BI2013a from a “Brain Invaders” experiment.

Dataset following the setup from [1] carried-out at University of Grenoble Alpes.

This dataset concerns an experiment carried out at GIPSA-lab (University of Grenoble Alpes, CNRS, Grenoble-INP) in 2013. The recordings concerned 24 subjects in total. Subjects 1 to 7 participated to eight sessions, run in different days, subject 8 to 24 participated to one session. Each session consisted in two runs, one in a Non-Adaptive (classical) and one in an Adaptive (calibration-less) mode of operation. The order of the runs was randomized for each session. In both runs there was a Training (calibration) phase and an Online phase, always passed in this order. In the non-Adaptive run the data from the Training phase was used for classifying the trials on the Online phase using the training-test version of the MDM algorithm [2]. In the Adaptive run, the data from the training phase was not used at all, instead the classifier was initialized with generic class geometric means and continuously adapted to the incoming data using the Riemannian method explained in [2]. Subjects were completely blind to the mode of operation and the two runs appeared to them identical.

In the Brain Invaders P300 paradigm, a repetition is composed of 12 flashes, of which 2 include the Target symbol (Target flashes) and 10 do not (non-Target flash). Please see [3] for a description of the paradigm. For this experiment, in the Training phases the number of flashes is fixed (80 Target flashes and 400 non-Target flashes). In the Online phases the number of Target and non-Target still are in a ratio 1/5, however their number is variable because the Brain Invaders works with a fixed number of game levels, however the number of repetitions needed to destroy the target (hence to proceed to the next level) depends on the user’s performance [2]. In any case, since the classes are unbalanced, an appropriate score must be used for quantifying the performance of classification methods (e.g., balanced accuracy, AUC methods, etc).

Data were acquired with a Nexus (TMSi, The Netherlands) EEG amplifier:

  • Sampling Frequency: 512 samples per second

  • Digital Filter: no

  • Electrodes: 16 wet Silver/Silver Chloride electrodes positioned at FP1, FP2, F5, AFz, F6, T7, Cz, T8, P7, P3, Pz, P4, P8, O1, Oz, O2 according to the 10/20 international system.

  • Reference: left ear-lobe.

  • Ground: N/A.

Principal Investigators:

Erwan Vaineau, Dr. Alexandre Barachant

Scientific Supervisor:

Dr. Marco Congedo

Technical Supervisor:

Anton Andreev

References

[1]

Vaineau, E., Barachant, A., Andreev, A., Rodrigues, P. C., Cattan, G. & Congedo, M. (2019). Brain invaders adaptive versus non-adaptive P300 brain-computer interface dataset. arXiv preprint arXiv:1904.09111.

[2] (1,2,3)

Barachant A, Congedo M (2014) A Plug & Play P300 BCI using Information Geometry. arXiv:1409.0107.

[3]

Congedo M, Goyat M, Tarrin N, Ionescu G, Rivet B,Varnet L, Rivet B, Phlypo R, Jrad N, Acquadro M, Jutten C (2011) “Brain Invaders”: a prototype of an open-source P300-based video game working with the OpenViBE platform. Proc. IBCI Conf., Graz, Austria, 280-283.

from moabb.datasets import BI2013a
dataset = BI2013a()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

24

#Chan

16

#Trials / class

3200 NT / 640 T

Trials length

1 s

Freq

512 Hz

#Sessions

8 for subjects 1-7 else 1

Participants

  • Population: healthy

  • Age: 25.96 (range: 20-30) years

  • BCI experience: volunteers recruited via flyers and university mailing list

Equipment

  • Amplifier: g.USBamp (g.tec, Schiedlberg, Austria)

  • Electrodes: wet Silver/Silver Chloride electrodes

  • Montage: standard_1020

  • Reference: left earlobe

Preprocessing

  • Data state: raw EEG with software tagging via USB (note: tagging introduces jitter and latency)

  • Notes: Tags sent by application to amplifier through USB port and recorded as supplementary channel; tagging process identical in all experimental conditions

Data Access

Experimental Protocol

  • Paradigm: p300

  • Task type: visual P300 BCI

  • Feedback: visual (Brain Invaders video game interface)

  • Stimulus: visual flashes

Notes

Note

BI2013a was previously named bi2013a. bi2013a will be removed in version 1.1.

__init__(non_adaptive=True, adaptive=False, training=True, online=False, subjects=None, sessions=None, *, return_all_modalities=False, **kwargs)[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])

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