Dynamic EEG compression approach with optimized distortion level for mobile health solutions

The development of a neurologically-oriented mobile health system involves significant challenges in terms of the proper sensing and efficient transmission of electroencephalogram (EEG) signals, and the faithful reconstruction of these signals at the receiving node. EEG compression has been widely used to reduce storage requirements, improve the real time processing of the sensed signals, and provide a better and timely feedback to the concerned patients. The non-stationarity of the EEG signals and the large volumes of data being continuously processed mandate the development of data reduction schemes that provide a good tradeoff between compression performance and the preservation of the signal quality and integrity. To this end, we propose in this work a dynamic and effective compression approach for EEG data that relies on a sequence of compression and decompression phases to optimize the compression rate while maintaining a distortion level below a target threshold. Simulation results using real EEG data segments show that even with stringent quality requirements, a notable compression ratio can be attained with minimal processing overhead.