Real-time non-uniform EEG sampling

Abstract

Lempel-Ziv algorithm can measure the complexity of a non-linear system. By improving the performance of this algorithm, we introduce a new non-uniform sampling method of EEG. Despite its low computational load for EEG compression, our method can sample it in real-time. The proposed method is a nonparametric technique that can compress EEG with a 1.97 compress ratio without prior knowledge, training, or preconditions. We evaluate the efficiency of our method in signal compression with common performance evaluation methods. By presenting another approach for evaluating the proposed method, we extract four non-linear features from EEG of sleep stages before and after applying our sampling method. The proposed method can increase non-linear feature distinguishability such as fractal dimension more than seven times by ignoring redundant samples. This study investigates the effect of redundant samples on EEG information richness. We demonstrate that our method by ignoring EEG useless samples can increase EEG information richness.