EEG datasets for motor imagery brain–computer interface

By Hohyun Cho, Minkyu Ahn, Sang-Kyun Ahn, Moonyoung Kwon, Sung Chan Jun. GigaScience, 2017

330 citations recorded by the source corpus.

Abstract Background: 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. Findings: We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information. Conclusions: Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states.

Subfields: Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/a78f491278f6305ebff54bad3dde664de85d210a

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Citations in the corpus

EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy (2019, methodology): “After each run, subjects filled out questionnaire II, which was designed with reference to [8], to check their current condition and to review the previously performed experiment (see Table 2 for details).”
Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review (2021, background): “[83] DS1: GigaDB [132] DS2: BCI-C IV-2a [131] c-sub: HO (8: 1 subs) (5: 1 subs) N/A _ _”