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

Ji Won Yoon, Stephen J. Roberts, Mathew Dyson, John Q. Gan

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

This paper proposes an algorithm for adaptive, sequential classification in systems with unknown labeling errors, focusing on the biomedical application of Brain Computer Interfacing (BCI). The method is shown to be robust in the presence of label and sensor noise. We focus on the inference and prediction of target labels under a nonlinear and non-Gaussian model. In order to handle missing or erroneous labeling, we model observed labels as a noisy observation of a latent label set with multiple classes (≥2). Whilst this paper focuses on the method's application to BCI systems, the algorithm has the potential to be applied to many application domains in which sequential missing labels are to be imputed in the presence of uncertainty. This dynamic classification algorithm combines an Ordered Probit model and an Extended Kalman Filter (EKF). The EKF estimates the parameters of the Ordered Probit model sequentially with time. We test the performance of the classification approach by processing synthetic datasets and real experimental EEG signals with multiple classes (2, 3 and 4 labels) for a Brain Computer Interfacing (BCI) experiment.

Original languageEnglish
Pages (from-to)726-734
Number of pages9
JournalNeural Networks
Volume24
Issue number7
DOIs
Publication statusPublished - 2011 Sep
Externally publishedYes

Keywords

  • Brain Computer Interfacing
  • Extended Kalman Filter
  • Multi-class classifier
  • Ordered Probit model
  • Sequential decisions

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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