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

Yoon, JW and Roberts, SJ and Dyson, M and Gan, JQ (2009) 'Adaptive classification for Brain Computer Interface systems using Sequential Monte Carlo sampling.' Neural Networks, 22 (9). 1286 - 1294. ISSN 0893-6080

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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 inference and prediction of target labels under a non-linear and non-Gaussian model. In this paper we introduce two new algorithms to handle missing or erroneous labeling in BCI data. One algorithm introduces auxiliary labels to process the uncertainty of the labels and the other modifies the optimal proposal functions to allow for uncertain labels. Although we focus on BCI problems in this paper, the algorithms can be generalized and applied to other application domains in which sequential missing labels are to be imputed under the presence of uncertainty. © 2009 Elsevier Ltd.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Users 161 not found.
Date Deposited: 17 Oct 2012 18:46
Last Modified: 23 Jan 2019 00:15
URI: http://repository.essex.ac.uk/id/eprint/4086

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