Dimensionality reduction with isomap algorithm for EEG covariance matrices

Egor Krivov, Mikhail Belyaev

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

11 Citations (Scopus)

Abstract

This paper presents new approach to braincomputer interface construction. Most algorithms for EEG classification use spatial covariance matrices, that contain information about synchronisation and desynchronisation in human brain. Suggested algorithm involves Riemannian geometry in the space of symmetric and positive-definite matrices to measure distances between covariance matrices in more accurate fashion. Then Isomap algorithm is applied to the Riemannian pairwise distances to locate manifold, corresponding to human EEG signals, and arrange points, corresponding to covariance matrices, in lowdimensional space, preserving geodesical distances. Finally, linear discriminant analysis is applied for classification. Suggested algorithm is tested on Berlin BCI dataset and compared with state-of-the-art algorithms common spatial patterns and classification in tangent space.

Original languageEnglish
Title of host publication4th International Winter Conference on Brain-Computer Interface, BCI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467378413
DOIs
Publication statusPublished - 20 Apr 2016
Externally publishedYes
Event4th International Winter Conference on Brain-Computer Interface, BCI 2016 - Gangwon Province, Korea, Republic of
Duration: 22 Feb 201624 Feb 2016

Publication series

Name4th International Winter Conference on Brain-Computer Interface, BCI 2016

Conference

Conference4th International Winter Conference on Brain-Computer Interface, BCI 2016
Country/TerritoryKorea, Republic of
CityGangwon Province
Period22/02/1624/02/16

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