Datasets

A dataset handle and abstract low level access to the data. the dataset will takes data stored locally, in the format in which they have been downloaded, and will convert them into a MNE raw object. There are options to pool all the different recording sessions per subject or to evaluate them separately.

Motor Imagery Datasets

AlexMI()

Alex Motor Imagery dataset.

BNCI2014001()

BNCI 2014-001 Motor Imagery dataset.

BNCI2014002()

BNCI 2014-002 Motor Imagery dataset.

BNCI2014004()

BNCI 2014-004 Motor Imagery dataset.

BNCI2015001()

BNCI 2015-001 Motor Imagery dataset.

BNCI2015004()

BNCI 2015-004 Motor Imagery dataset.

Cho2017()

Motor Imagery dataset from Cho et al 2017.

Lee2019_MI([train_run, test_run, …])

Methods

MunichMI()

Munich Motor Imagery dataset.

Ofner2017([imagined, executed])

Motor Imagery ataset from Ofner et al 2017.

PhysionetMI([imagined, executed])

Physionet Motor Imagery dataset.

Schirrmeister2017()

High-gamma dataset discribed in Schirrmeister et al. 2017.

Shin2017A([accept])

Motor Imagey Dataset from Shin et al 2017

Shin2017B([accept])

Mental Arithmetic Dataset from Shin et al 2017

Weibo2014()

Motor Imagery dataset from Weibo et al 2014.

Zhou2016()

Motor Imagery dataset from Zhou et al 2016.

ERP Datasets

bi2013a([NonAdaptive, Adaptive, Training, …])

P300 dataset bi2013a from a “Brain Invaders” experiment

BNCI2014008()

BNCI 2014-008 P300 dataset

BNCI2014009()

BNCI 2014-009 P300 dataset.

BNCI2015003()

BNCI 2015-003 P300 dataset.

DemonsP300()

Visual P300 dataset recorded in Virtual Reality (VR) game Raccoons versus Demons.

EPFLP300()

P300 dataset from Hoffmann et al 2008.

Lee2019_ERP([train_run, test_run, …])

Methods

SSVEP Datasets

SSVEPExo()

SSVEP Exo dataset

Nakanishi2015()

SSVEP Nakanishi 2015 dataset

Wang2016()

SSVEP Wang 2016 dataset

MAMEM1()

SSVEP MAMEM 1 dataset

MAMEM2()

SSVEP MAMEM 2 dataset

MAMEM3()

SSVEP MAMEM 3 dataset

Lee2019_SSVEP([train_run, test_run, …])

Methods

Base & Utils

base.BaseDataset(subjects, …[, doi, …])

Parameters required for all datasets

download.data_path(url, sign[, path, …])

Get path to local copy of given dataset URL.

download.data_dl(url, sign[, path, …])

Download file from url to specified path

download.fs_issue_request(method, url, headers)

Wrapper for HTTP request

download.fs_get_file_list(article_id[, version])

List all the files associated with a given article.

download.fs_get_file_hash(filelist)

Returns a dict associating figshare file id to MD5 hash

download.fs_get_file_id(filelist)

Returns a dict associating filename to figshare file id

download.fs_get_file_name(filelist)

Returns a dict associating figshare file id to filename

utils.dataset_search(paradigm[, …])

Returns a list of datasets that match a given criteria

utils.find_intersecting_channels(datasets[, …])

Given a list of dataset instances return a list of channels shared by all datasets.