TY - GEN
T1 - A Novel Framework for Visual Motion Imagery Classification Using 3D Virtual BCI Platform
AU - Kwon, Byoung Hee
AU - Jeong, Ji Hoon
AU - Kim, Dong Joo
N1 - Funding Information:
Research was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00432, Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User’s Thought via AR/VR Interface) and partly funded by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - In this study, 3D brain-computer interface (BCI) training platforms were used to stimulate the subjects for visual motion imagery and visual perception. We measured the activation brain region and alpha-band power activity when the subjects perceived and imagined the stimuli. Based on this, 4-class were classified in visual stimuli session and visual motion imagery session respectively. The results showed that the occipital region is involved in visual perception and visual motion imagery, and alpha-band power is increased in visual motion imagery session and decreased in visual motion stimuli session. Compared with the performance of visual motion imagery and motor imagery, visual motion imagery has higher performance than motor imagery. The binary class was classified using one versus rest approach as well as analysis of brain activation to prove that visual-related brain wave signals are meaningful, and the results were significant.
AB - In this study, 3D brain-computer interface (BCI) training platforms were used to stimulate the subjects for visual motion imagery and visual perception. We measured the activation brain region and alpha-band power activity when the subjects perceived and imagined the stimuli. Based on this, 4-class were classified in visual stimuli session and visual motion imagery session respectively. The results showed that the occipital region is involved in visual perception and visual motion imagery, and alpha-band power is increased in visual motion imagery session and decreased in visual motion stimuli session. Compared with the performance of visual motion imagery and motor imagery, visual motion imagery has higher performance than motor imagery. The binary class was classified using one versus rest approach as well as analysis of brain activation to prove that visual-related brain wave signals are meaningful, and the results were significant.
KW - 3D BCI training plotform
KW - brain-computer interface
KW - electroencephalography
KW - robotic arm
KW - visual motion imagery
UR - http://www.scopus.com/inward/record.url?scp=85084077648&partnerID=8YFLogxK
U2 - 10.1109/BCI48061.2020.9061621
DO - 10.1109/BCI48061.2020.9061621
M3 - Conference contribution
AN - SCOPUS:85084077648
T3 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
BT - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
Y2 - 26 February 2020 through 28 February 2020
ER -