Research on the methods for multi-class kernel CSP-based feature extraction / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 217-222, 2012.
Article
in Chinese
| WPRIM (Western Pacific)
| ID: wpr-274869
Responsible library:
WPRO
ABSTRACT
To relax the presumption of strictly linear patterns in the common spatial patterns (CSP), we studied the kernel CSP (KCSP). A new multi-class KCSP (MKCSP) approach was proposed in this paper, which combines the kernel approach with multi-class CSP technique. In this approach, we used kernel spatial patterns for each class against all others, and extracted signal components specific to one condition from EEG data sets of multiple conditions. Then we performed classification using the Logistic linear classifier. Brain computer interface (BCI) competition III_3a was used in the experiment. Through the experiment, it can be proved that this approach could decompose the raw EEG singles into spatial patterns extracted from multi-class of single trial EEG, and could obtain good classification results.
Full text:
Available
Database:
WPRIM (Western Pacific)
Main subject:
Physiology
/
Algorithms
/
Signal Processing, Computer-Assisted
/
User-Computer Interface
/
Brain
/
Pattern Recognition, Automated
/
Data Interpretation, Statistical
/
Electroencephalography
/
Brain-Computer Interfaces
/
Methods
Language:
Chinese
Journal:
Journal of Biomedical Engineering
Year:
2012
Document type:
Article