moabb.datasets.Wang2016#

class moabb.datasets.Wang2016(subjects=None, sessions=None)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Wang2016

A benchmark SSVEP dataset acquired with a 40-target BCI speller using joint frequency and phase modulation (JFPM) approach

SSVEP, 40 classes

AuthorsYijun Wang, Xiaogang Chen, Xiaorong Gao, Shangkai Gao

πŸ‡¨πŸ‡³β€‚Tsinghua University, CNΒ·2016Β·wangyj@semi.ac.cn
SSVEP Code: Wang2016 34 subjects 1 session 64 ch 250 Hz 40 classes 6.0 s trials CC BY 4.0

Class Labels: 8, 9, 10, 11, 12, 13, 14, 15, ...

Read Paper Compare Similar Report Issue

Overview

SSVEP Wang 2016 dataset.

Dataset from

This dataset gathered SSVEP-BCI recordings of 35 healthy subjects (17 females, aged 17-34 years, mean age: 22 years) focusing on 40 characters flickering at different frequencies (8-15.8 Hz with an interval of 0.2 Hz). For each subject, the experiment consisted of 6 blocks. Each block contained 40 trials corresponding to all 40 characters indicated in a random order. Each trial started with a visual cue (a red square) indicating a target stimulus. The cue appeared for 0.5 s on the screen. Subjects were asked to shift their gaze to the target as soon as possible within the cue duration. Following the cue offset, all stimuli started to flicker on the screen concurrently and lasted 5 s. After stimulus offset, the screen was blank for 0.5 s before the next trial began, which allowed the subjects to have short breaks between consecutive trials. Each trial lasted a total of 6 s. To facilitate visual fixation, a red triangle appeared below the flickering target during the stimulation period. In each block, subjects were asked to avoid eye blinks during the stimulation period. To avoid visual fatigue, there was a rest for several minutes between two consecutive blocks.

EEG data were acquired using a Synamps2 system (Neuroscan, Inc.) with a sampling rate of 1000 Hz. The amplifier frequency passband ranged from 0.15 Hz to 200 Hz. Sixty-four channels covered the whole scalp of the subject and were aligned according to the international 10-20 system. The ground was placed on midway between Fz and FPz. The reference was located on the vertex. Electrode impedances were kept below 10 KΞ©. To remove the common power-line noise, a notch filter at 50 Hz was applied in data recording. Event triggers generated by the computer to the amplifier and recorded on an event channel synchronized to the EEG data.

The continuous EEG data was segmented into 6 s epochs (500 ms pre-stimulus, 5.5 s post-stimulus onset). The epochs were subsequently downsampled to 250 Hz. Thus each trial consisted of 1500 time points. Finally, these data were stored as double-precision floating-point values in MATLAB and were named as subject indices (i.e., S01.mat, …, S35.mat). For each file, the data loaded in MATLAB generate a 4-D matrix named β€˜data’ with dimensions of [64, 1500, 40, 6]. The four dimensions indicate β€˜Electrode index’, β€˜Time points’, β€˜Target index’, and β€˜Block index’. The electrode positions were saved in a β€˜64-channels.loc’ file. Six trials were available for each SSVEP frequency. Frequency and phase values for the 40 target indices were saved in a β€˜Freq_Phase.mat’ file.

Information for all subjects was listed in a β€˜Sub_info.txt’ file. For each subject, there are five factors including β€˜Subject Index’, β€˜Gender’, β€˜Age’, 'Handedness', and 'Group'. Subjects were divided into an 'experienced' group (eight subjects, S01-S08) and a 'naive' group (27 subjects, S09-S35) according to their experience in SSVEP-based BCIs.

Open in Overview tab β†’

Benchmark Context

WithinSession

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

Sample frame: 34 subjects Γ— 1 sessions

  • SSVEP all classes 6 pipelinesMax 67.52 Β· Median 29.39 Β· Mean 31.86 Β· Std 31.78

Citation & Impact

  • Paper DOI10.1109/TNSRE.2016.2627556
  • CitationsLoading…
  • Public APICrossref | OpenAlex
  • MOABB tables1 (WithinSession)
  • Page Views
    30d: 34 Β· all-time: 510
    #18 of 151 Β· Top 12% most viewed
    Updated: 2026-03-20 UTC
Stimulus Protocol
../_images/Wang2016.svg

6s task window per trial Β· 40-class ssvep paradigm Β· 1 runs/session across 1 sessions

HED Event Tags
HED tags40/40 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus
40
Label
40
Sensory-event
40
Visual-presentation
40
8
Sensory-eventExperimental-stimulusVisual-presentationLabel
9
Sensory-eventExperimental-stimulusVisual-presentationLabel
10
Sensory-eventExperimental-stimulusVisual-presentationLabel
11
Sensory-eventExperimental-stimulusVisual-presentationLabel
12
Sensory-eventExperimental-stimulusVisual-presentationLabel
13
Sensory-eventExperimental-stimulusVisual-presentationLabel
14
Sensory-eventExperimental-stimulusVisual-presentationLabel
15
Sensory-eventExperimental-stimulusVisual-presentationLabel
8.2
Sensory-eventExperimental-stimulusVisual-presentationLabel
9.2
Sensory-eventExperimental-stimulusVisual-presentationLabel
10.2
Sensory-eventExperimental-stimulusVisual-presentationLabel
11.2
Sensory-eventExperimental-stimulusVisual-presentationLabel
12.2
Sensory-eventExperimental-stimulusVisual-presentationLabel
13.2
Sensory-eventExperimental-stimulusVisual-presentationLabel
14.2
Sensory-eventExperimental-stimulusVisual-presentationLabel
15.2
Sensory-eventExperimental-stimulusVisual-presentationLabel
8.4
Sensory-eventExperimental-stimulusVisual-presentationLabel
9.4
Sensory-eventExperimental-stimulusVisual-presentationLabel
10.4
Sensory-eventExperimental-stimulusVisual-presentationLabel
11.4
Sensory-eventExperimental-stimulusVisual-presentationLabel
12.4
Sensory-eventExperimental-stimulusVisual-presentationLabel
13.4
Sensory-eventExperimental-stimulusVisual-presentationLabel
14.4
Sensory-eventExperimental-stimulusVisual-presentationLabel
15.4
Sensory-eventExperimental-stimulusVisual-presentationLabel
8.6
Sensory-eventExperimental-stimulusVisual-presentationLabel
9.6
Sensory-eventExperimental-stimulusVisual-presentationLabel
10.6
Sensory-eventExperimental-stimulusVisual-presentationLabel
11.6
Sensory-eventExperimental-stimulusVisual-presentationLabel
12.6
Sensory-eventExperimental-stimulusVisual-presentationLabel
13.6
Sensory-eventExperimental-stimulusVisual-presentationLabel
14.6
Sensory-eventExperimental-stimulusVisual-presentationLabel
15.6
Sensory-eventExperimental-stimulusVisual-presentationLabel
8.8
Sensory-eventExperimental-stimulusVisual-presentationLabel
9.8
Sensory-eventExperimental-stimulusVisual-presentationLabel
10.8
Sensory-eventExperimental-stimulusVisual-presentationLabel
11.8
Sensory-eventExperimental-stimulusVisual-presentationLabel
12.8
Sensory-eventExperimental-stimulusVisual-presentationLabel
13.8
Sensory-eventExperimental-stimulusVisual-presentationLabel
14.8
Sensory-eventExperimental-stimulusVisual-presentationLabel
15.8
Sensory-eventExperimental-stimulusVisual-presentationLabel

HED tree view

Tree Β· 8
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 9
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 10
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 11
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 12
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 13
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 14
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 15
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 8.2
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 9.2
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 10.2
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 11.2
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 12.2
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 13.2
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 14.2
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 15.2
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 8.4
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 9.4
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 10.4
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 11.4
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 12.4
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 13.4
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 14.4
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 15.4
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 8.6
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 9.6
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 10.6
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 11.6
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 12.6
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 13.6
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 14.6
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 15.6
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 8.8
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 9.8
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 10.8
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 11.8
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 12.8
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 13.8
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 14.8
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Tree Β· 15.8
β”œβ”€ Sensory-event
β”œβ”€ Experimental-stimulus
β”œβ”€ Visual-presentation
└─ Label
Channel Summary
Total channels64
EEG64
Montage10-05
Sampling250 Hz
ReferenceCz
Filter{'bandpass': [0.15, 200], 'notch': 50}
Notch / line50 Hz

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

SSVEP Wang 2016 dataset.

Dataset from [1].

This dataset gathered SSVEP-BCI recordings of 35 healthy subjects (17 females, aged 17-34 years, mean age: 22 years) focusing on 40 characters flickering at different frequencies (8-15.8 Hz with an interval of 0.2 Hz). For each subject, the experiment consisted of 6 blocks. Each block contained 40 trials corresponding to all 40 characters indicated in a random order. Each trial started with a visual cue (a red square) indicating a target stimulus. The cue appeared for 0.5 s on the screen. Subjects were asked to shift their gaze to the target as soon as possible within the cue duration. Following the cue offset, all stimuli started to flicker on the screen concurrently and lasted 5 s. After stimulus offset, the screen was blank for 0.5 s before the next trial began, which allowed the subjects to have short breaks between consecutive trials. Each trial lasted a total of 6 s. To facilitate visual fixation, a red triangle appeared below the flickering target during the stimulation period. In each block, subjects were asked to avoid eye blinks during the stimulation period. To avoid visual fatigue, there was a rest for several minutes between two consecutive blocks.

EEG data were acquired using a Synamps2 system (Neuroscan, Inc.) with a sampling rate of 1000 Hz. The amplifier frequency passband ranged from 0.15 Hz to 200 Hz. Sixty-four channels covered the whole scalp of the subject and were aligned according to the international 10-20 system. The ground was placed on midway between Fz and FPz. The reference was located on the vertex. Electrode impedances were kept below 10 KΞ©. To remove the common power-line noise, a notch filter at 50 Hz was applied in data recording. Event triggers generated by the computer to the amplifier and recorded on an event channel synchronized to the EEG data.

The continuous EEG data was segmented into 6 s epochs (500 ms pre-stimulus, 5.5 s post-stimulus onset). The epochs were subsequently downsampled to 250 Hz. Thus each trial consisted of 1500 time points. Finally, these data were stored as double-precision floating-point values in MATLAB and were named as subject indices (i.e., S01.mat, …, S35.mat). For each file, the data loaded in MATLAB generate a 4-D matrix named β€˜data’ with dimensions of [64, 1500, 40, 6]. The four dimensions indicate β€˜Electrode index’, β€˜Time points’, β€˜Target index’, and β€˜Block index’. The electrode positions were saved in a β€˜64-channels.loc’ file. Six trials were available for each SSVEP frequency. Frequency and phase values for the 40 target indices were saved in a β€˜Freq_Phase.mat’ file.

Information for all subjects was listed in a β€˜Sub_info.txt’ file. For each subject, there are five factors including β€˜Subject Index’, β€˜Gender’, β€˜Age’, β€˜Handedness’, and β€˜Group’. Subjects were divided into an β€˜experienced’ group (eight subjects, S01-S08) and a β€˜naive’ group (27 subjects, S09-S35) according to their experience in SSVEP-based BCIs.

Warning

The original dataset includes two channels labeled β€˜CB1’ and β€˜CB2’, which are not part of the standard 10-20 EEG montage. Although the authors of Wang2016 state that the 10-20 layout was used, the provided channel location file suggests that β€˜CB1’ and β€˜CB2’ may correspond approximately to β€˜P9’ and β€˜P10’. However, this mapping is not confirmed, and the exact locations remain uncertain.

In this implementation, we treat β€˜CB1’ and β€˜CB2’ as standard EEG channels, following the approach used by the authors.

Users should be aware of this ambiguity when interpreting spatial analyses or when comparing to other datasets with strictly standard montages.

References

[1]

Wang, Y., Chen, X., Gao, X., & Gao, S. (2016). A benchmark dataset for SSVEP-based brain–computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10), 1746-1752. doi: 10.1109/TNSRE.2016.2627556.

from moabb.datasets import Wang2016
dataset = Wang2016()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

34

#Chan

64

#Classes

40

#Trials / class

6

Trials length

5 s

Freq

250 Hz

#Sessions

1

Participants

  • Population: healthy

  • Age: 22 (range: 17-34) years

  • BCI experience: 8 experienced, 27 naΓ―ve

Equipment

  • Amplifier: Synamps2 EEG system (Neuroscan, Inc.)

  • Montage: standard_1005

  • Reference: Cz

Preprocessing

  • Data state: Raw epochs extracted from continuous EEG recordings according to stimulus onsets, downsampled to 250 Hz, no digital filters applied

  • Steps: Epoch extraction according to stimulus onsets from event channel, Downsampling from 1000 Hz to 250 Hz, No digital filters applied in preprocessing

  • Notes: Data epochs include 0.5 s before stimulus onset, 5 s for stimulation, and 0.5 s after stimulus offset. Upper bound frequency of SSVEP harmonics is around 90 Hz.

Data Access

Experimental Protocol

  • Paradigm: ssvep

  • Stimulus: 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, 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 β€œ~/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 β€œ~/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 β€œfixed” 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

Examples using moabb.datasets.Wang2016#

Dataset bubble plot

Dataset bubble plot