EEG datasets for motor imagery brain–computer interface

• Published in: Gigascience Vol. 6; no. 7; pp. 1 - 8
• Main Authors: Cho, Hohyun, Ahn, Minkyu, Ahn, Sangtae, Kwon, Moonyoung, Jun, Sung Chan
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.07.2017
• Subjects: Adult; Biochips; Brain; Brain-Computer Interfaces; Cerebral Cortex - physiology; Computer applications; Data Note; Datasets; Datasets as Topic - standards; EEG; Electroencephalography; Electroencephalography - methods; Electroencephalography - standards; Female; Human factors; Human-computer interface; Humans; Imagery; Imagination; Implants; Male; Mental task performance; Movement; Questionnaires; Software; Somatosensory cortex; Synchronism; Synchronization; Variation; motor imagery; brain–computer interface; EEG; subject-to-subject transfer; performance variation
• ISSN: 2047-217X; 2047-217X
• DOI: 10.1093/gigascience/gix034

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.