EEG SIGNAL COMPRESSION USING RADIAL BASIS NEURAL NETWORKS

This paper describes a two-stage lossless compression scheme for electroencephalographic (EEG) signals using radial basis neural network predictors. Two variants of the radial basis network, namely, the radial basis function network and the generalized regression neural network are used in the first stage and their performances are evaluated in terms of compression ratio. The training is imparted to the network by using two training schemes, namely, single block scheme and block adaptive scheme. The compression ratios achieved by these networks when used along with arithmetic encoders in a two-stage compression scheme are obtained for different EEG test files. It is found that the generalized regression neural network performs better than other neural network models such as multilayer perceptrons and Elman network and linear predictor such as FIR.