2009 Special Issue: Adaptive classification for Brain Computer Interface systems using Sequential Monte Carlo sampling

Authors:
Ji Won Yoon;Stephen J. Roberts;Matt Dyson;John Q. Gan
Affiliations:
Pattern Analysis & Machine Learning Research Group, Department of Engineering Science, University of Oxford, Oxford, UK;Pattern Analysis & Machine Learning Research Group, Department of Engineering Science, University of Oxford, Oxford, UK;Department of Computing and Electronic Systems, University of Essex, UK;Department of Computing and Electronic Systems, University of Essex, UK
Venue:
Neural Networks
Year:
2009

Citing 5
Cited 3

Brain-computer interface: a new communication device for handicapped persons

Journal of Microcomputer Applications - Special issue on computer applications for handicapped persons
On sequential Monte Carlo sampling methods for Bayesian filtering

Statistics and Computing
An introduction to ROC analysis

Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Covariate Shift Adaptation by Importance Weighted Cross Validation

The Journal of Machine Learning Research
Monte Carlo Strategies in Scientific Computing

Monte Carlo Strategies in Scientific Computing

Editorial: Recent advances in brain-machine interfaces

Neural Networks
Bayesian inference for an adaptive Ordered Probit model: An application to Brain Computer Interfacing

Neural Networks
A sequential dynamic multi-class model and recursive filtering by variational bayesian methods

PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

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.