Design and experiment of a multi-modal electroencephalogram-near infrared spectroscopy helmet for simultaneously acquiring at the same brain area.
10.7507/1001-5515.201611025
- Author:
Xin XIONG
1
;
Yunfa FU
2
;
Xiabing ZHANG
1
;
Song LI
1
;
Baolei XU
3
;
Xuxian YIN
3
Author Information
1. School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming 650500, P.R.China.
2. School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming 650500, P.R.China.fyf@ynu.edu.cn.
3. State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, P.R.China.
- Publication Type:Journal Article
- Keywords:
brain-computer interface;
electroencephalogram;
electroencephalogram-near infrared spectroscopy helmet;
multi-modal;
near infrared spectroscopy
- From:
Journal of Biomedical Engineering
2018;35(2):290-296
- CountryChina
- Language:Chinese
-
Abstract:
Multi-modal brain-computer interface and multi-modal brain function imaging are developing trends for the present and future. Aiming at multi-modal brain-computer interface based on electroencephalogram-near infrared spectroscopy (EEG-NIRS) and in order to simultaneously acquire the brain activity of motor area, an acquisition helmet by NIRS combined with EEG was designed and verified by the experiment. According to the 10-20 system or 10-20 extended system, the diameter and spacing of NIRS probe and EEG electrode, NIRS probes were aligned with C3 and C4 as the reference electrodes, and NIRS probes were placed in the middle position between EEG electrodes to simultaneously measure variations of NIRS and the corresponding variation of EEG in the same functional brain area. The clamp holder and near infrared probe were coupled by tightening a screw. To verify the feasibility and effectiveness of the multi-modal EEG-NIRS helmet, NIRS and EEG signals were collected from six healthy subjects during six mental tasks involving the right hand clenching force and speed motor imagery. These signals may reflect brain activity related to hand clenching force and speed motor imagery in a certain extent. The experiment showed that the EEG-NIRS helmet designed in the paper was feasible and effective. It not only could provide support for the multi-modal motor imagery brain-computer interface based on EEG-NIRS, but also was expected to provide support for multi-modal brain functional imaging based on EEG-NIRS.
