moabb.datasets.Huebner2017#

class moabb.datasets.Huebner2017(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

Huebner2017

P300 / ERP, 2 classes (Target vs NonTarget)

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

🇩🇪 Albert-Ludwigs-University, DE·2017·david.huebner@blbt.uni-freiburg.de
P300 / ERP Code: Huebner2017 13 subjects 3 sessions 31 ch 1000 Hz 2 classes 25.0 s trials CC BY 4.0

Class Labels: Target, NonTarget

Read Paper Compare Similar Report Issue

Overview

Learning from label proportions for a visual matrix speller (ERP) dataset from Hübner et al 2017

Dataset description

The subjects were asked to spell the sentence: “Franzy jagt im komplett verwahrlosten Taxi quer durch Freiburg”. The sentence was chosen because it contains each letter used in German at least once. Each subject spelled this sentence three times. The stimulus onset asynchrony (SOA) was 250 ms (corresponding to 15 frames on the LCD screen utilized) while the stimulus duration was 100 ms (corresponding to 6 frames on the LCD screen utilized). For each character, 68 highlighting events occurred and a total of 63 characters were spelled three times. This resulted in a total of 68 ⋅ 63 ⋅ 3 = 12852 EEG epochs per subject. Spelling one character took around 25 s including 4 s for cueing the current symbol, 17 s for highlighting and 4 s to provide feedback to the user. Assuming a perfect decoding, these timing constraints would allow for a maximum spelling speed of 2.4 characters per minute. Fig 1 shows the complete experimental structure and how LLP is used to reconstruct average target and non-target ERP responses.

Subjects were placed in a chair at 80 cm distance from a 24-inch flat screen. EEG signals from 31 passive Ag/AgCl electrodes (EasyCap) were recorded, which were placed approximately equidistantly according to the extended 10–20 system, and whose impedances were kept below 20 kΩ. All channels were referenced against the nose and the ground was at FCz. The signals were registered by multichannel EEG amplifiers (BrainAmp DC, Brain Products) at a sampling rate of 1 kHz. To control for vertical ocular movements and eye blinks, we recorded with an EOG electrode placed below the right eye and referenced against the EEG channel Fp2 above the eye. In addition, pulse and breathing activity were recorded.

: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

Open in Overview tab →

Benchmark Context

WithinSession

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

Sample frame: 13 subjects × 3 sessions

  • ERP/P300 all classes 5 pipelinesMax 98.69 · Median 97.74 · Mean 97.04 · Std 1.70

Citation & Impact

Stimulus Protocol
../_images/Huebner2017.svg

25s task window per trial · 2-class p300 / erp paradigm · 9 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 (passive Ag/AgCl)
MISC6
Montagestandard_1020
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.

Learning from label proportions for a visual matrix speller (ERP) dataset from Hübner et al 2017 [1].

Dataset description

The subjects were asked to spell the sentence: “Franzy jagt im komplett verwahrlosten Taxi quer durch Freiburg”. The sentence was chosen because it contains each letter used in German at least once. Each subject spelled this sentence three times. The stimulus onset asynchrony (SOA) was 250 ms (corresponding to 15 frames on the LCD screen utilized) while the stimulus duration was 100 ms (corresponding to 6 frames on the LCD screen utilized). For each character, 68 highlighting events occurred and a total of 63 characters were spelled three times. This resulted in a total of 68 ⋅ 63 ⋅ 3 = 12852 EEG epochs per subject. Spelling one character took around 25 s including 4 s for cueing the current symbol, 17 s for highlighting and 4 s to provide feedback to the user. Assuming a perfect decoding, these timing constraints would allow for a maximum spelling speed of 2.4 characters per minute. Fig 1 shows the complete experimental structure and how LLP is used to reconstruct average target and non-target ERP responses.

Subjects were placed in a chair at 80 cm distance from a 24-inch flat screen. EEG signals from 31 passive Ag/AgCl electrodes (EasyCap) were recorded, which were placed approximately equidistantly according to the extended 10–20 system, and whose impedances were kept below 20 kΩ. All channels were referenced against the nose and the ground was at FCz. The signals were registered by multichannel EEG amplifiers (BrainAmp DC, Brain Products) at a sampling rate of 1 kHz. To control for vertical ocular movements and eye blinks, we recorded with an EOG electrode placed below the right eye and referenced against the EEG channel Fp2 above the eye. In addition, pulse and breathing activity were recorded.

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]

Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., & Kindermans, P. J. (2017) Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees. PLOS ONE 12(4): e0175856. https://doi.org/10.1371/journal.pone.0175856

from moabb.datasets import Huebner2017
dataset = Huebner2017()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

13

#Chan

31

#Trials / class

364 NT / 112 T

Trials length

0.9 s

Freq

1000 Hz

#Sessions

3

Participants

  • Population: healthy

  • Age: 26 years

  • BCI experience: mostly naive

Equipment

  • Amplifier: BrainAmp DC

  • Electrodes: passive Ag/AgCl

  • Montage: standard_1020

  • Reference: nose

Preprocessing

  • Data state: raw

Data Access

Experimental Protocol

  • Paradigm: p300

  • Feedback: visual

  • Stimulus: character matrix

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)[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.

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