moabb.datasets.Huebner2018#

class moabb.datasets.Huebner2018(interval=None, raw_slice_offset=None, use_blocks_as_sessions=True, subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: _BaseVisualMatrixSpellerDataset

[source]

Dataset Snapshot

Huebner2018

P300 / ERP, 2 classes (Target vs NonTarget)

AuthorsDavid Hübner, Thibault Verhoeven, Klaus-Robert Müller, Pieter-Jan Kindermans, Michael Tangermann

🇩🇪 University of Freiburg, DE·2018·p.kindermans@tu-berlin.de
P300 / ERP Code: Huebner2018 12 subjects 3 sessions 31 ch 1000 Hz 2 classes 17.0 s trials CC BY 4.0

Class Labels: Target, NonTarget

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Overview

Mixture of LLP and EM for a visual matrix speller (ERP) dataset from Hübner et al 2018

Dataset description

Within a single session, a subject was asked to spell the beginning of a sentence in each of three blocks.The text consists of the 35 symbols “Franzy jagt im Taxi quer durch das ”. Each block, one of the three decoding algorithms (EM, LLP, MIX) was used in order to guess the attended symbol. The order of the blocks was pseudo-randomized over subjects, such that each possible order of the three decoding algorithms was used twice. This randomization should reduce systematic biases by order effects or temporal effects, e.g., due to fatigue or task-learning.

A trial describes the process of spelling one character. Each of the 35 trials per block contained 68 highlighting events. The stimulus onset asynchrony (SOA) was 250 ms and the stimulus duration was 100 ms leading to an interstimulus interval (ISI) of 150 ms.

:param interval: range/interval in milliseconds in which the brain response/activity relative to an event/stimulus onset lies in. Default is set to [-.2, .7]. :type interval: array_like :param raw_slice_offset: defines the crop offset in milliseconds before the first and after the last event (target or non-targeet) onset. Default None which crops with an offset 2,000 ms. :type raw_slice_offset: int, None

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Benchmark Context

WithinSession

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

Sample frame: 12 subjects × 3 sessions

  • ERP/P300 all classes 5 pipelinesMax 98.47 · Median 97.54 · Mean 97.11 · Std 1.28

Citation & Impact

  • Paper DOI10.1109/MCI.2018.2807039
  • CitationsLoading…
  • Public APICrossref | OpenAlex
  • MOABB tables1 (WithinSession)
  • Page Views
    30d: 6 · all-time: 132
    #46 of 151 · Top 31% most viewed
    Updated: 2026-03-18 UTC
Stimulus Protocol
../_images/Huebner2018.svg

17s task window per trial · 2-class p300 / erp paradigm · 1 runs/session across 3 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 channels31
EEG31 (Ag/AgCl)
MISC6
Montageextended 10-20
Sampling1000 Hz
Referencenose
Notch / line50 Hz

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

Mixture of LLP and EM for a visual matrix speller (ERP) dataset from Hübner et al 2018 [1].

Dataset description

Within a single session, a subject was asked to spell the beginning of a sentence in each of three blocks.The text consists of the 35 symbols “Franzy jagt im Taxi quer durch das ”. Each block, one of the three decoding algorithms (EM, LLP, MIX) was used in order to guess the attended symbol. The order of the blocks was pseudo-randomized over subjects, such that each possible order of the three decoding algorithms was used twice. This randomization should reduce systematic biases by order effects or temporal effects, e.g., due to fatigue or task-learning.

A trial describes the process of spelling one character. Each of the 35 trials per block contained 68 highlighting events. The stimulus onset asynchrony (SOA) was 250 ms and the stimulus duration was 100 ms leading to an interstimulus interval (ISI) of 150 ms.

param interval:

range/interval in milliseconds in which the brain response/activity relative to an event/stimulus onset lies in. Default is set to [-.2, .7].

type interval:

array_like

param raw_slice_offset:

defines the crop offset in milliseconds before the first and after the last event (target or non-targeet) onset. Default None which crops with an offset 2,000 ms.

type raw_slice_offset:

int, None

References

[1]

Huebner, D., Verhoeven, T., Mueller, K. R., Kindermans, P. J., & Tangermann, M. (2018). Unsupervised learning for brain-computer interfaces based on event-related potentials: Review and online comparison [research frontier]. IEEE Computational Intelligence Magazine, 13(2), 66-77. https://doi.org/10.1109/MCI.2018.2807039

from moabb.datasets import Huebner2018
dataset = Huebner2018()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

12

#Chan

31

#Trials / class

364 NT / 112 T

Trials length

0.9 s

Freq

1000 Hz

#Sessions

3

Participants

  • Population: healthy

  • Age: 26 (range: 19-31) years

  • BCI experience: mixed

Equipment

  • Amplifier: BrainAmp DC

  • Electrodes: Ag/AgCl

  • Montage: extended 10-20

  • Reference: nose

Preprocessing

  • Data state: raw

Data Access

Experimental Protocol

  • Paradigm: p300

  • Tasks: copy-spelling

  • Feedback: visual

  • Stimulus: modified matrix speller with flexible highlighting

Added in version 0.4.5.

__init__(interval=None, raw_slice_offset=None, use_blocks_as_sessions=True, 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, 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