IMPROVING THE PERFORMANCE OF SPARSE REPRESENTATION-BASED CLASSIFIER FOR EEG CLASSIFICATION

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

MIRJALILI ALIREZA; ABOOTALEBI VAHID; SADEGHI MOHAMMAD TAGHI

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: SIGNAL AND DATA PROCESSING Year:2015 | Volume:- | Issue:3 (SERIAL 25) Page(s): 43-55

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

2,010

Download:

Cites:

Information Journal Paper

Title

IMPROVING THE PERFORMANCE OF SPARSE REPRESENTATION-BASED CLASSIFIER FOR EEG CLASSIFICATION

Author(s)

MIRJALILI ALIREZA | ABOOTALEBI VAHID | SADEGHI MOHAMMAD TAGHI | Issue Writer Certificate

Keywords

ELECTROENCEPHALOGRAMQ2

SPARSE REPRESENTATION-BASED CLASSIFIERQ2

REGULARIZED COMMON SPATIAL PATTERNSQ2

SMOOTHED L0-NORMQ2

Abstract

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.

Cites

No record.

References

No record.

Cite

APA: Copy

MIRJALILI, ALIREZA, ABOOTALEBI, VAHID, & SADEGHI, MOHAMMAD TAGHI. (2015). IMPROVING THE PERFORMANCE OF SPARSE REPRESENTATION-BASED CLASSIFIER FOR EEG CLASSIFICATION. SIGNAL AND DATA PROCESSING, -(3 (SERIAL 25)), 43-55. SID. https://sid.ir/paper/160874/en

Vancouver: Copy

MIRJALILI ALIREZA, ABOOTALEBI VAHID, SADEGHI MOHAMMAD TAGHI. IMPROVING THE PERFORMANCE OF SPARSE REPRESENTATION-BASED CLASSIFIER FOR EEG CLASSIFICATION. SIGNAL AND DATA PROCESSING[Internet]. 2015;-(3 (SERIAL 25)):43-55. Available from: https://sid.ir/paper/160874/en

IEEE: Copy

ALIREZA MIRJALILI, VAHID ABOOTALEBI, and MOHAMMAD TAGHI SADEGHI, “IMPROVING THE PERFORMANCE OF SPARSE REPRESENTATION-BASED CLASSIFIER FOR EEG CLASSIFICATION,” SIGNAL AND DATA PROCESSING, vol. -, no. 3 (SERIAL 25), pp. 43–55, 2015, [Online]. Available: https://sid.ir/paper/160874/en

Related Journal Papers