상위피인용논문
부산대학교
Abstract
M. Jawad Khan1, Melissa Jiyoun Hong2 and Keum-Shik Hong1,3*
1Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, Republic of Korea
2Department of Education Policy and Social Analysis, Columbia University, New York, NY, USA
3School of Mechanical Engineering, Pusan National University, Busan, Republic of Korea
*Correspondence: Keum-Shik Hong, Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Guemjeong-gu, Busan 609-735, Republic of Korea
Abstract
The hybrid brain-computer interface (BCI)'s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, “forward,” “backward,” “left,” and “right.” The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology.
논문정보
관련 링크
연구자 키워드
연구자 ID
관련분야 연구자보기
소속기관 논문보기
관련분야 논문보기
해당논문 저자보기