Learning On-Line Classification via Decorrelated LMS Algorithm: Application to Brain-Computer Interfaces

This paper addresses the challenge of neurophysiological signal classification arising from brain-computer interface (BCI) applications and proposes an on-line classifier designed via the decorrelated least mean square (LMS) algorithm, based on a Bayesian classifier with Gaussian mixture models.

Abstract

The classification of time-varying neurophysiological signals, e.g., electroencephalogram (EEG) signals, advances the requirement of adaptability for classifiers. In this paper we address the challenge of neurophysiological signal classification arising from brain-computer interface (BCI) applications and propose an on-line classifier designed via the decorrelated least mean square (LMS) algorithm. Based on a Bayesian classifier with Gaussian mixture models, we derive the general formulation of gradient descent algorithms under the criterion of LMS. Further, to accelerate convergence, the decorrelated gradient instead of the instantaneous gradient is adopted for updating the parameters of the classifier adaptively. Utilizing the presented classifier for the off-line analysis of practical classification tasks in brain-computer interface applications shows its effectiveness and robustness compared to the stochastic gradient descent classifier which uses the instantaneous gradient directly.