A Study of Combined Lossy Compression and Person Identification on EEG Signals

Biometric information extracted from electroencephalogram (EEG) signals is being used increasingly in person identification systems thanks to several advantages, compared to traditional ones such as fingerprint, face and voice. However, one of the major challenges is that a huge amount of EEG data needs to be processed, transmitted and stored. The use of EEG compression is therefore becoming necessary. Although the lossy compression technique gives a higher Compression Ratio (CR) than lossless ones, they introduce the loss of information in recovered signals, which may affect to the performance of EEG-based person identification systems. In this paper, we investigate the impact of lossy compression on EEG data used in EEG-based person identification systems. Experimental results demonstrate that in the best case, CR could achieve up to 70 with minimal loss of person identification performance, and using EEG lossy compression is feasible compared to using lossless one.