Recurrent neural network predictors for EEG signal compression
F. Bartolini, Vito Cappellini, Sebastiano Nerozzi, A. Mecocci · 2002
The progress of digital electroencephalography gave rise to the problem of EEG data recording. In the paper a DPCM scheme for EEG signal compression is discussed. In particular the performance of a class of predictors based on recurrent neural networks is presented. The training strategy is accurately described and the results of a comparison with some other classical linear and static neural predictors are given. The proposed recurrent neural predictor demonstrates to be competitive with the others in offering good performance at a very low computational cost.