Filter bank extension for neural network-based motor imagery classification

Pavel Merinov, Mikhail Belyaev, Egor Krivov

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

1 Citation (Scopus)

Abstract

One of the most successful Motor Imagery classification methods is the Common Spatial Pattern algorithm, which is used as a feature generation method, combined with the Linear Discriminant Analysis classifier. CSP parameters are estimated via optimizing of a criterion implicitly connected to classification accuracy. Many modifications of CSP were proposed, but almost all of them just adjust an objective function in an optimization problem. Another extension is the Filter Bank CSP algorithm, which combines CSP features calculated in different frequency bands. So, parameters of such classification pipelines are estimated in two steps: 1) solving an optimization problem to generate features and 2) create a linear classifier based on these features. In this work, we propose to combine these two steps into a single optimization problem to build an FBCSP-like model.

Original languageEnglish
Title of host publication2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
EditorsKostas Diamantaras, Aurelio Uncini, Francesco A. N. Palmieri, Jan Larsen
PublisherIEEE Computer Society
ISBN (Electronic)9781509007462
DOIs
Publication statusPublished - 8 Nov 2016
Externally publishedYes
Event26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
Duration: 13 Sep 201616 Sep 2016

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2016-November
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
Country/TerritoryItaly
CityVietri sul Mare, Salerno
Period13/09/1616/09/16

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