Context Based Error Modeling for Lossless Compression of EEG Signals Using Neural Networks

Abstract

Two-stage lossless data compression methods involving predictors and encoders are well known. This paper discusses the application of context based error modeling techniques for neural network predictors used for the compression of EEG signals. Error modeling improves the performance of a compression algorithm by removing the statistical redundancy that exists among the error signals after the prediction stage. In this paper experiments are carried out by using human EEG signals recorded under various physiological conditions to evaluate the effect of context based error modeling in the EEG compression. It is found that the compression efficiency of the neural network based predictive techniques is significantly improved by using the error modeling schemes. It is shown that the bits per sample required for EEG compression with error modeling and entropy coding lie in the range of 2.92 to 6.62 which indicates a saving of 0.3 to 0.7 bits compared to the compression scheme without error modeling.

A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors

“…Context-based error modeling has gained much research importance in improving the performance of compression algorithms [4, 22, 42, 43]. It is known that most state-of-the- art lossless coding techniques comprise prediction, context modeling of prediction error followed by entropy encoding [42].…”

“…Context modeling of prediction error is a means to separate these distributions thereby adjusting the offset to yield zero Laplacian distribution. Such scheme is referred to as context-based bias cancellation [4, 22, 42, 43] and has been applied to EEG signals using linear predictors [4] and neural network predictors [22]. In [4, 22] contexts were framed by computing the difference between the adjacent two samples.…”

“…Such scheme is referred to as context-based bias cancellation [4, 22, 42, 43] and has been applied to EEG signals using linear predictors [4] and neural network predictors [22]. In [4, 22] contexts were framed by computing the difference between the adjacent two samples. An improved context calculation is introduced in this work, where the context for each sample is computed by determining the difference between the past sample and next sample as shown in (3).…”

“…Two-stage lossless compression schemes involving predictors and entropy encoders have been reported in [4, 10–14, 21–23]. In [4, 22], context- based offset bias cancellation has been applied to the predictive error to improve the distribution of residues suitable for encoding. Agarwal and Gotman [15] discussed EEG compression schemes for intensive care applications.…”

Estimation and interpolation of underwater low frequency ambient noise spectrum using artificial neural networks

“…While these studies explained the relation of Noise level with wind speed and wave by expression, considerable variability was observed in the coefficients and hence in the estimation [9]. Neural networks possess attractive properties such as massive parallelism, robustness, adaptive learning, self-organization, fault tolerance and generalization which are useful for enhancing the performance of a predictor [15]. In order to carry out the detection procedure the neural network needs to be trained.…”