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Parallel Model Feature Extraction to Improve Performance of a BCI System
- Source :
- Journal of Institute of Control, Robotics and Systems. 19:1022-1028
- Publication Year :
- 2013
- Publisher :
- Institute of Control, Robotics and Systems, 2013.
-
Abstract
- It is well knowns that based on the CSP (Common Spatial Pattern) algorithm, the linear projection of an EEG (Electroencephalography) signal can be made to spaces that optimize the discriminant between two patterns. Sharing disadvantages from linear time invariant systems, CSP suffers from the non-stationary nature of EEGs causing the performance of the classification in a BCI (Brain-Computer Interface) system to drop significantly when comparing the training data and test data. The author has suggested a simple idea based on the parallel model of CSP filters to improve the performance of BCI systems. The model was tested with a simple CSP algorithm (without any elaborate regularizing methods) and a perceptron learning algorithm as a classifier to determine the improvement of the system. The simulation showed that the parallel model could improve classification performance by over 10% compared to conventional CSP methods.
- Subjects :
- Training set
Computer science
business.industry
Applied Mathematics
Feature extraction
Pattern recognition
Perceptron
Machine learning
computer.software_genre
LTI system theory
Discriminant
Control and Systems Engineering
Artificial intelligence
business
Classifier (UML)
computer
Software
Brain–computer interface
Test data
Subjects
Details
- ISSN :
- 19765622
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- Journal of Institute of Control, Robotics and Systems
- Accession number :
- edsair.doi...........669b2c310917304d036a04e3e845f0f6