Adaptive Multiclass Classification for Brain Computer Interfaces
We consider the problem of multiclass adaptive classification for brain-computer interfaces and propose the use of multiclass pooled mean linear discriminant analysis (MPMLDA), a multiclass generalization of the adaptation rule introduced by Vidaurre, Kawanabe, von Bünau, Blankertz, and Müller ( ) f...
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Published in | Neural computation Vol. 26; no. 6; pp. 1108 - 1127 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
One Rogers Street, Cambridge, MA 02142-1209, USA
MIT Press
01.06.2014
MIT Press Journals, The |
Subjects | |
Online Access | Get full text |
ISSN | 0899-7667 1530-888X 1530-888X |
DOI | 10.1162/NECO_a_00592 |
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Summary: | We consider the problem of multiclass adaptive classification for brain-computer interfaces and propose the use of multiclass pooled mean linear discriminant analysis (MPMLDA), a multiclass generalization of the adaptation rule introduced by Vidaurre, Kawanabe, von Bünau, Blankertz, and Müller (
) for the binary class setting. Using publicly available EEG data sets and tangent space mapping (Barachant, Bonnet, Congedo, & Jutten,
) as a feature extractor, we demonstrate that MPMLDA can significantly outperform state-of-the-art multiclass static and adaptive methods. Furthermore, efficient learning rates can be achieved using data from different subjects. |
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Bibliography: | June, 2014 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Correspondence-1 content type line 23 |
ISSN: | 0899-7667 1530-888X 1530-888X |
DOI: | 10.1162/NECO_a_00592 |