moabb.datasets.Liu2022EldBETA#

class moabb.datasets.Liu2022EldBETA(subjects=None, sessions=None)[source]#

Bases: BaseDataset

Dataset Snapshot

Liu2022EldBETA

SSVEP, 9 classes

AuthorsBingchuan Liu, Yijun Wang, Xiaorong Gao, Xiaogang Chen

🇨🇳 Tsinghua University, CN·2022

SSVEP Code: Liu2022EldBETA 100 subjects 7 sessions 64 ch 1000 Hz 9 classes 5.0 s trials CC BY 4.0

Class Labels: 8, 9.5, 11, 8.5, 10, 11.5, 9, 10.5, ...

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Overview

eldBETA SSVEP benchmark dataset for elderly population.

Dataset from

The eldBETA database contains 64-channel EEG recordings from 100 elderly participants (33 males, 67 females, aged 51-81, mean age 63.17) performing a 9-target SSVEP-BCI task. Stimuli used joint frequency and phase modulation (JFPM) with 9 targets in a 3x3 matrix. Frequencies ranged from 8.0 to 12.0 Hz (0.5 Hz step).

Each subject completed 7 blocks of 9 trials. Each trial consisted of a 4 s target cue followed by 5 s of SSVEP stimulation and 1 s rest (10 s total per trial). EEG was recorded at 1000 Hz with a Synamps2 system (Neuroscan) using 64 channels.

Data is loaded from the BIDS-formatted GDF files included in each subject's Figshare archive. The GDF files contain continuous recordings at 1000 Hz with event annotations marking each stimulus onset.

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Citation & Impact

Paper DOI10.1038/s41597-022-01372-9
CitationsLoading…
Public APICrossref | OpenAlex
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Updated: 2026-03-12 UTC

Stimulus Protocol

../_images/Liu2022EldBETA.svg

5s task window per trial · 9-class ssvep paradigm · 1 runs/session across 7 sessions

HED Event Tags

HED tags9/9 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus

9

Label

9

Sensory-event

9

Visual-presentation

9

8

Sensory-eventExperimental-stimulusVisual-presentationLabel

9.5

Sensory-eventExperimental-stimulusVisual-presentationLabel

11

Sensory-eventExperimental-stimulusVisual-presentationLabel

8.5

Sensory-eventExperimental-stimulusVisual-presentationLabel

10

Sensory-eventExperimental-stimulusVisual-presentationLabel

11.5

Sensory-eventExperimental-stimulusVisual-presentationLabel

9

Sensory-eventExperimental-stimulusVisual-presentationLabel

10.5

Sensory-eventExperimental-stimulusVisual-presentationLabel

12

Sensory-eventExperimental-stimulusVisual-presentationLabel

HED tree view

Tree · 8

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 9.5

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 11

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 8.5

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 10

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 11.5

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 9

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 10.5

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 12

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Channel Summary

Total channels64

EEG64

Montage10-05

Sampling1000 Hz

ReferenceCz

Notch / line50 Hz

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

Overview

eldBETA SSVEP benchmark dataset for elderly population.

Dataset from [1].

The eldBETA database contains 64-channel EEG recordings from 100 elderly participants (33 males, 67 females, aged 51-81, mean age 63.17) performing a 9-target SSVEP-BCI task. Stimuli used joint frequency and phase modulation (JFPM) with 9 targets in a 3x3 matrix. Frequencies ranged from 8.0 to 12.0 Hz (0.5 Hz step).

Each subject completed 7 blocks of 9 trials. Each trial consisted of a 4 s target cue followed by 5 s of SSVEP stimulation and 1 s rest (10 s total per trial). EEG was recorded at 1000 Hz with a Synamps2 system (Neuroscan) using 64 channels.

Data is loaded from the BIDS-formatted GDF files included in each subject’s Figshare archive. The GDF files contain continuous recordings at 1000 Hz with event annotations marking each stimulus onset.

Warning

The GDF files in the archive are mislabeled with .edf extension and contain an extra header block from the biosig4octave exporter. This adapter patches the header on-the-fly before reading.

Like Wang2016 and BETA, this dataset uses the same 64-channel Tsinghua Neuroscan cap layout including ‘CB1’ and ‘CB2’ channels.

References

[1]

B. Liu, Y. Wang, X. Gao, and X. Chen, “eldBETA: A Large Eldercare-oriented Benchmark Database of SSVEP-BCI for the Aging Population,” Scientific Data, vol. 9, p. 252, 2022. DOI: 10.1038/s41597-022-01372-9

Code Examples

from moabb.datasets import Liu2022EldBETA
dataset = Liu2022EldBETA()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	100
#Chan	64
#Classes	9
#Trials / class	7
Trials length	6 s
Freq	1000 Hz
#Sessions	7

Participants

Population: healthy
Age: 63.17 (range: 51-81) years

Equipment

Amplifier: Synamps2 (Neuroscan)
Montage: standard_1005
Reference: Cz

Data Access

DOI: 10.1038/s41597-022-01372-9
Data URL: https://doi.org/10.6084/m9.figshare.18032669
Repository: Figshare

Experimental Protocol

Paradigm: ssvep
Task type: 9-target SSVEP speller
Feedback: visual
Stimulus: JFPM visual flicker

__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)[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:

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]#

Return list of 7 GDF file paths (one per session/block).

Downloads and extracts the subject’s Figshare tar.gz archive if needed.

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