moabb.datasets.Nakanishi2015#

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

SSVEP Nakanishi 2015 dataset.

PapersWithCode leaderboard: https://paperswithcode.com/dataset/nakanishi2015-moabb

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

Experimental Protocol

  • Paradigm: ssvep

  • Feedback: none

  • Stimulus: flickering

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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

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