A TrAdaBoost-based method for detecting multiple subjects' P300 potentials.
10.7507/1001-5515.201811025
- Author:
Guizhi XU
1
,
2
;
Fang LIN
1
,
2
;
Minghong GONG
1
,
2
;
Mengfan LI
1
,
3
;
Hongli YU
1
,
2
Author Information
1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China
2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China.
3. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China.mfli@hebut.edu.cn.
- Publication Type:Journal Article
- Keywords:
P300;
TrAdaBoost;
brain-computer interface;
linear discriminant analysis classifier;
support vector machine;
transfer learning
- MeSH:
Algorithms;
Brain-Computer Interfaces;
Discriminant Analysis;
Electroencephalography;
Event-Related Potentials, P300;
Humans;
Support Vector Machine
- From:
Journal of Biomedical Engineering
2019;36(4):531-540
- CountryChina
- Language:Chinese
-
Abstract:
Individual differences of P300 potentials lead to that a large amount of training data must be collected to construct pattern recognition models in P300-based brain-computer interface system, which may cause subjects' fatigue and degrade the system performance. TrAdaBoost is a method that transfers the knowledge from source area to target area, which improves learning effect in the target area. Our research purposed a TrAdaBoost-based linear discriminant analysis and a TrAdaBoost-based support vector machine to recognize the P300 potentials across multiple subjects. This method first trains two kinds of classifiers separately by using the data deriving from a small amount of data from same subject and a large amount of data from different subjects. Then it combines all the classifiers with different weights. Compared with traditional training methods that use only a small amount of data from same subject or mixed different subjects' data to directly train, our algorithm improved the accuracies by 19.56% and 22.25% respectively, and improved the information transfer rate of 14.69 bits/min and 15.76 bits/min respectively. The results indicate that the TrAdaBoost-based method has the potential to enhance the generalization ability of brain-computer interface on the individual differences.
