On EEG lossy data compression for data-intensive neurological mobile health solutions

Neurologically-oriented m-health applications are characterized by the recording, transmission, and processing of large volumes of EEG data. This places a significant load on the systems´ components in terms of storage capacity, processing capabilities, etc. Data compression has been proposed as one technique to reduce the amount of data originating from the sensing node to the processing node. While lossless compression was considered the method of choice due to the critical aspect of preserving the features of EEG data, in this work, we propose an aggressive lossy/lossless hybrid scheme that provides a good tradeoff between compression performance and feature preservation by adaptively varying the data percentage which is being compressed in a lossless or lossy manner. Simulation results using real EEG data segments show the high compression ratio that can be achieved while preserving the signal quality.