EEG datasets for motor imagery brain–computer interface | Zendy

Hohyun Cho | Zendy; Minkyu Ahn | Zendy; Sangtae Ahn | Zendy; Moonyoung Kwon | Zendy; Sung Chan Jun | Zendy

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EEG datasets for motor imagery brain–computer interface

Author(s) -

Hohyun Cho,

Minkyu Ahn,

Sangtae Ahn,

Moonyoung Kwon,

Sung Chan Jun

Publication year - 2017

Publication title -

gigascience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.947

H-Index - 54

ISSN - 2047-217X

DOI - 10.1093/gigascience/gix034

Subject(s) - brain–computer interface , motor imagery , electroencephalography , computer science , interface (matter) , brain activity and meditation , task (project management) , human–computer interaction , neuroscience , psychology , management , bubble , maximum bubble pressure method , parallel computing , economics

Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states.

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