Recognition of three different imagined movement of the right foot based on functional near-infrared spectroscopy.
10.7507/1001-5515.201905001
- Author:
Yu LI
1
;
Xin XIONG
1
;
Zhaoyang LI
1
;
Yunfa FU
1
Author Information
1. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P.R.China;Integration and Innovation team of Brain Cognition and Brain Computer Intelligence, Kunming University of Science and Technology, Kunming 650500, P.R.China;Key Laboratory of Computer Technology Application in Yunnan Province, Kunming 650500, P.R.China.
- Publication Type:Journal Article
- Keywords:
brain-computer interface;
classification and recognition;
correlation coefficient;
functional near-infrared spectroscopy;
motor imagery
- MeSH:
Brain;
diagnostic imaging;
Brain-Computer Interfaces;
Foot;
Humans;
Imagination;
Movement;
Spectroscopy, Near-Infrared
- From:
Journal of Biomedical Engineering
2020;37(2):262-270
- CountryChina
- Language:Chinese
-
Abstract:
Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is a new-type human-computer interaction technique. To explore the separability of fNIRS signals in different motor imageries on the single limb, the study measured the fNIRS signals of 15 subjects (amateur football fans) during three different motor imageries of the right foot (passing, stopping and shooting). And the correlation coefficient of the HbO signal during different motor imageries was extracted as features for the input of a three-classification model based on support vector machines. The results found that the classification accuracy of the three motor imageries of the right foot was 78.89%±6.161%. The classification accuracy of the two-classification of motor imageries of the right foot, that is, passing and stopping, passing and shooting, and stopping and shooting was 85.17%±4.768%, 82.33%±6.011%, and 89.33%±6.713%, respectively. The results demonstrate that the fNIRS of different motor imageries of the single limb is separable, which is expected to add new control commands to fNIRS-BCI and also provide a new option for rehabilitation training and control peripherals for unilateral stroke patients. Besides, the study also confirms that the correlation coefficient can be used as an effective feature to classify different motor imageries.
