Volume 12, Issue 3 (12-2015)                   JSDP 2015, 12(3): 43-55 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Mirjalili A, Abootalebi V, Sadeghi M T. Improving the performance of sparse representation-based classifier for EEG classification. JSDP 2015; 12 (3) :43-55
URL: http://jsdp.rcisp.ac.ir/article-1-175-en.html
Yazd University
Abstract:   (6255 Views)

In this paper, the problem of classification of motor imagery EEG signals using a sparse representation-based classifier is considered. Designing a powerful dictionary matrix, i.e. extracting proper features, is an important issue in such a classifier. Due to its high performance, the Common Spatial Patterns (CSP) algorithm is widely used for this purpose in the BCI systems. The main disadvantages of the CSP algorithm are its sensibility to noise and the over learning phenomena when the number of training samples is limited. In this study, to overcome these problems, two modified form of the CSP algorithms, namely the DLRCSP and GLRCSP have been used. Using the adopted methods, the average detection rate is increased by a factor of about 7.78 %. Also, a problem of the SRC classifier which uses the standard BP algorithm is the computational complexity of the BP algorithm. To overcome this weakness, we used a new algorithm which is called the SL0 algorithm. Our classification results show that using the SL0 algorithm, the classification process is highly speeded up. Moreover, it leads to an increase of about 1.61% in average correct detection compared to the basic standard algorithm.

Full-Text [PDF 3488 kb]   (2042 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2013/10/17 | Accepted: 2015/09/8 | Published: 2016/01/4 | ePublished: 2016/01/4

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2015 All Rights Reserved | Signal and Data Processing