A TrAdaBoost-based method for detecting multiple subjects' P300 potentials / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 531-540, 2019.
Article
in Chinese
| WPRIM
| ID: wpr-774174
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Algorithms
/
Discriminant Analysis
/
Event-Related Potentials, P300
/
Electroencephalography
/
Support Vector Machine
/
Brain-Computer Interfaces
Limits:
Humans
Language:
Chinese
Journal:
Journal of Biomedical Engineering
Year:
2019
Type:
Article
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