Correlation-Based Regularized Common Spatial Patterns for Classification of Motor Imagery EEG Signals

Khatereh Darvish Ghanbar, Tohid Yousefi Rezaii, Mohammad Ali Tinati, Ali Farzamnia

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

3 Citations (Scopus)


Common Spatial Patterns (CSP) is a powerful and common method for effective feature extraction and dimensionality reduction in Brain-Computer Interface (BCI) applications. However CSP has some shortcomings, particularly, it is sensitivity to noise and outlier data which results in lower classification accuracy. In this paper, we propose a regularized version of the original CSP (Corr-CSP), in which the objective function is penalized by a properly designed penalty term which encourages decorrelation between the data from two classes in such a way that the resulting objective function has still straightforward solution through Eigen value decomposition. Furthermore, we have used three different datasets from the BCI Competition BCI database in order to evaluate the performance of the proposed approach and compare it to the original CSP. The simulation results show on the average 4% of improvement in terms of classification accuracy for the proposed Corr-CSP approach.

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)9781728115092
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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