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Adaptive classification for Brain Computer Interface systems using Sequential Monte Carlo sampling.

Abstract:

Adaptive classification is a key function of Brain Computer Interfacing (BCI) systems. This paper proposes robust mathematical frameworks and their implementation for the on-line sequential classification of EEG signals in BCI systems. The proposed algorithms are extensions to the basic method of Andrieu et al. [Andrieu, C., de Freitas, N., and Doucet, A. (2001). Sequential bayesian semi-parametric binary classification. In Proc. NIPS], modified to be suitable for BCI use. We focus on the inf...

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Publication status:
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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Journal:
Neural networks : the official journal of the International Neural Network Society
Volume:
22
Issue:
9
Pages:
1286-1294
Publication date:
2009-11-05
DOI:
EISSN:
1879-2782
ISSN:
0893-6080
URN:
uuid:99f975ec-4d40-4325-9c96-8d92b5ba3aef
Source identifiers:
62599
Local pid:
pubs:62599

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