Analysis and research of brain-computer interface experiments for imaging left-right hands movement.
- Author:
Yazhou WU
1
;
Qinghua HE
;
Hua HUANG
;
Ling ZHANG
;
Yu ZHUO
;
Qi XIE
;
Baoming WU
Author Information
1. Fifth Department of Da-ping Hospital & Research Institute of Surgery, Third Military Medical University, Chongqing 400042, China.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Brain;
physiology;
Electroencephalography;
methods;
Evoked Potentials, Motor;
physiology;
Hand;
physiology;
Humans;
Movement;
physiology;
Neural Networks (Computer);
Pattern Recognition, Physiological;
Signal Processing, Computer-Assisted;
Thinking;
physiology;
User-Computer Interface
- From:
Journal of Biomedical Engineering
2008;25(5):983-988
- CountryChina
- Language:Chinese
-
Abstract:
This is a research carried out to explore a pragmatic way of BCI based imaging movement, i. e. to extract the feature of EEG for reflecting different thinking by searching suitable methods of signal extraction and recognition algorithm processing, to boost the recognition rate of communication for BCI system, and finally to establish a substantial theory and experimental support for BCI application. In this paper, different mental tasks for imaging left-right hands movement from 6 subjects were studied in three different time sections (hint keying at 2s, 1s and 0s after appearance of arrow). Then we used wavelet analysis and Feed-forward Back-propagation Neural Network (BP-NN) method for processing and analyzing the experimental data of off-line. Delay time delta t2, delta t1 and delta t0 for all subjects in the three different time sections were analyzed. There was significant difference between delta to and delta t2 or delta t1 (P<0.05), but no significant difference was noted between delta t2 and delta t1 (P>0.05). The average results of recognition rate were 65%, 86.67% and 72%, respectively. There were obviously different features for imaging left-right hands movement about 0.5-1s before actual movement; these features displayed significant difference. We got higher recognition rate of communication under the hint keying at about 1s after the appearance of arrow. These showed the feasibility of using the feature signals extracted from the project as the external control signals for BCI system, and demon strated that the project provided new ideas and methods for feature extraction and classification of mental tasks for BCI.
