Research on the methods for multi-class kernel CSP-based feature extraction.
- Author:
Jinjia WANG
1
;
Lingzhi ZHANG
;
Bei HU
Author Information
1. College of Information Science and Engineer, Yanshan University, Qinhuangdao 066004, China. wjj@ysu.edu.cn
- Publication Type:Journal Article
- MeSH:
Algorithms;
Brain;
physiology;
Brain-Computer Interfaces;
Data Interpretation, Statistical;
Electroencephalography;
methods;
Pattern Recognition, Automated;
methods;
Signal Processing, Computer-Assisted;
instrumentation;
User-Computer Interface
- From:
Journal of Biomedical Engineering
2012;29(2):217-222
- CountryChina
- Language:Chinese
-
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.
