moabb.datasets.Nakanishi2015#

class moabb.datasets.Nakanishi2015(subjects=None, sessions=None)[source]#

Bases: BaseDataset

Dataset Snapshot

Nakanishi2015

A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials. This study performed a comparison of existing CCA-based SSVEP detection methods using a 12-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment.

SSVEP, 12 classes

AuthorsMasaki Nakanishi, Yijun Wang, Yu-Te Wang, Tzyy-Ping Jung

🇺🇸 University of California San Diego, US·2015·wangyj@semi.ac.cn

SSVEP Code: Nakanishi2015 9 subjects 1 session 8 ch 256 Hz 12 classes 4.0 s trials Unknown

Class Labels: 9.25, 11.25, 13.25, 9.75, 11.75, 13.75, 10.25, 12.25, ...

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Overview

SSVEP Nakanishi 2015 dataset.

This dataset contains 12-class joint frequency-phase modulated steady-state visual evoked potentials (SSVEPs) acquired from 10 subjects used to estimate an online performance of brain-computer interface (BCI) in the reference study

Open in Overview tab →

Benchmark Context

WithinSession

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

Sample frame: 9 subjects × 1 sessions

SSVEP all classes 6 pipelinesMax 87.22 · Median 80.99 · Mean 58.46 · Std 39.49

Citation & Impact

Paper DOI10.1371/journal.pone.0140703
CitationsLoading…
Public APICrossref | OpenAlex
MOABB tables1 (WithinSession)
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30d: 31 · all-time: 458
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Updated: 2026-03-20 UTC

Stimulus Protocol

../_images/Nakanishi2015.svg

4s task window per trial · 12-class ssvep paradigm · 1 runs/session across 1 sessions

HED Event Tags

HED tags12/12 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus

12

Label

12

Sensory-event

12

Visual-presentation

12

9.25

Sensory-eventExperimental-stimulusVisual-presentationLabel

11.25

Sensory-eventExperimental-stimulusVisual-presentationLabel

13.25

Sensory-eventExperimental-stimulusVisual-presentationLabel

9.75

Sensory-eventExperimental-stimulusVisual-presentationLabel

11.75

Sensory-eventExperimental-stimulusVisual-presentationLabel

13.75

Sensory-eventExperimental-stimulusVisual-presentationLabel

10.25

Sensory-eventExperimental-stimulusVisual-presentationLabel

12.25

Sensory-eventExperimental-stimulusVisual-presentationLabel

14.25

Sensory-eventExperimental-stimulusVisual-presentationLabel

10.75

Sensory-eventExperimental-stimulusVisual-presentationLabel

12.75

Sensory-eventExperimental-stimulusVisual-presentationLabel

14.75

Sensory-eventExperimental-stimulusVisual-presentationLabel

HED tree view

Tree · 9.25

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

Tree · 11.25

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

Tree · 13.25

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

Tree · 9.75

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

Tree · 11.75

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

Tree · 13.75

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

Tree · 10.25

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

Tree · 12.25

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

Tree · 14.25

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

Tree · 10.75

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

Tree · 12.75

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

Tree · 14.75

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

Channel Summary

Total channels8

EEG8 (EEG)

Montagestandard_1020

Sampling256 Hz

ReferenceCMS/DRL

Notch / line60 Hz

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

Overview

SSVEP Nakanishi 2015 dataset.

This dataset contains 12-class joint frequency-phase modulated steady-state visual evoked potentials (SSVEPs) acquired from 10 subjects used to estimate an online performance of brain-computer interface (BCI) in the reference study [1].

References

[1]

Masaki Nakanishi, Yijun Wang, Yu-Te Wang and Tzyy-Ping Jung, “A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials,” PLoS One, vol.10, no.10, e140703, 2015. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140703

Code Examples

from moabb.datasets import Nakanishi2015
dataset = Nakanishi2015()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	9
#Chan	8
#Classes	12
#Trials / class	15
Trials length	4.15 s
Freq	256 Hz
#Sessions	1

Participants

Population: healthy
Age: 28 years
BCI experience: not specified

Equipment

Amplifier: Biosemi ActiveTwo
Electrodes: EEG
Montage: standard_1020
Reference: CMS/DRL

Preprocessing

Bandpass filter: 6-80 Hz
Steps: downsampling, bandpass filtering
Notes: Zero-phase forward and reverse IIR filtering was implemented using the filtfilt() function in MATLAB. Data epochs were extracted with a 135-ms latency delay considering the visual system delay.

Data Access

DOI: 10.1371/journal.pone.0140703
Data URL: mnakanishi/12JFPM_SSVEP
Repository: Github

Experimental Protocol

Paradigm: ssvep
Feedback: none
Stimulus: flickering

__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:

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:

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