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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Bibliographic Details
Published inNeural computation Vol. 26; no. 6; pp. 1108 - 1127
Main Authors Llera, A., Gómez, V., Kappen, H. J.
Format Journal Article
LanguageEnglish
Published One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.06.2014
MIT Press Journals, The
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ISSN0899-7667
1530-888X
1530-888X
DOI10.1162/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.
Bibliography:June, 2014
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ISSN:0899-7667
1530-888X
1530-888X
DOI:10.1162/NECO_a_00592