Neural Comput - Adaptive multiclass classification for brain computer interfaces.

Tópicos

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{ group(2977) signific(1463) compar(1072) }
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{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
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{ survey(1388) particip(1329) question(1065) }
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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

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 B?nau, Blankertz, and M?ller (2010) for the binary class setting. Using publicly available EEG data sets and tangent space mapping (Barachant, Bonnet, Congedo, & Jutten, 2012) 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.

Resumo Limpo

consid problem multiclass adapt classif braincomput interfac propos use multiclass pool mean linear discrimin analysi mpmlda multiclass general adapt rule introduc vidaurr kawanab von bnau blankertz mller binari class set use public avail eeg data set tangent space map barach bonnet congedo jutten featur extractor demonstr mpmlda can signific outperform stateoftheart multiclass static adapt method furthermor effici learn rate can achiev use data differ subject

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