Transputer Implementation of the EKF-Based Learning Algorithm for Multilayered Neural Networks used in Classification of EEG Signals

Abstract

Artificial neural network approaches for classification of EEG signals using the widely known back-propagation algorithm to train the network are reported in the literature. However, the speed of convergence of the backpropagation algorithm is rather slow. The Extended Kalman Filtering (EKF) based learning algorithm which has a much faster convergence speed is suggested for training the neural network used in classification of EEG signals.

A further reduction in computation time is possible if paralle processing of the EKF based learning algorithm is introduced. Systolic and wavefront arrays have been suggested as suitable architectures for parallel processing. The transputer is one such architecture which has been specially desinged for use as a processing node in a parallel processing network. Transputer implementation of the EKF based learning algorithm for multilayered neural network used in classification of EEG signals is the subject matter of this paper.