Brain-Computer Interface Binary Classification using Regularized CSP and Stacked Concept
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2016 by IJETT Journal|
|Year of Publication : 2016|
|Authors : B. Bijitha, Nandakumar Paramparambath
|DOI : 10.14445/22315381/IJETT-V38P249|
B. Bijitha, Nandakumar Paramparambath"Brain-Computer Interface Binary Classification using Regularized CSP and Stacked Concepte", International Journal of Engineering Trends and Technology (IJETT), V38(5),271-275 August 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Brain-Computer Interface technology is the one in which the brain signals acquired from scalp recordings are used to control external devices like artificial limbs, computers, etc. Even though studies on BCI technology are progressing, a consistent algorithm that will work with all types of data and environment are not developed so far. In this paper, an algorithm with feature extraction using regularized version of CSP and PCA, then the features are classified using the stacked concept classifier. The algorithm is evaluated using kappa coefficient and compared with existing algorithms.
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brain computer interfaces, EEG, feature extraction, common spatial pattern, classification, regularized linear discriminant analysis.