Online Dictionary Learning for Sparse Representation-Based Classification of Motor Imagery EEG

Vahid Sharghian, Tohid Yousefi Rezaii, Ali Farzamnia, Mohammad Ali Tinati

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)


The use of Motor Imagery (MI) EEG signals in implementation of Brain-Computer Interfaces (BCIs) has been increased during recent years. Simpler interaction of users with these types of interfaces in comparison with other interfaces like BCI systems based on steady state visually evoked potential (SSVEP) as well as comprehensiveness of motor-imagery-based interfaces are the most important reasons of this popularity. In this paper, we propose to use Sparse Representation-based Classification (SRC) method for MI-EEG based BCI. In order to obtain more accurate sparse representation of the underlying EEG signal, we used dictionary learning approach instead of deterministic dictionary structures which leads to better classification performance. Therefore, we have used Correlation-Based Least Squares Update (CBLSU) in order to obtain an online dictionary learning scheme which is more suitable for BCI systems in terms of computational complexity. It is demonstrated that the proposed method shows better performance in comparison with the existing methods which more commonly use linear classifiers like LDA and SVM. The mean accuracy of 84.79 (SD = 8.51) is obtained for five subjects of dataset IVa from BCI Competition III database, which shows considerable improvement compared to the existing methods.

Original languageEnglish
Title of host publicationICEE 2019 - 27th Iranian Conference on Electrical Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781728115085, 9781728115078
ISBN (Print)978128115092
Publication statusPublished - 5 Aug 2019
Externally publishedYes
Event27th Iranian Conference on Electrical Engineering - Yazd, Iran, Islamic Republic of
Duration: 30 Apr 20192 May 2019
Conference number: 27

Publication series

NameIranian Conference on Electrical Engineering
ISSN (Print)2164-7054
ISSN (Electronic)2642-9527


Conference27th Iranian Conference on Electrical Engineering
Abbreviated titleICEE 2019
Country/TerritoryIran, Islamic Republic of

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