Noise-Masking Cryptosystem Using Watermark and Chain Generation for EEG Measurement with Compressed Sensing

For long-term wireless electroencephalogram (EEG) measurements, it is crucial for the device to have both low power consumption and robust security. To address these requirements, we propose a noise-masking cryptosystem using a watermark and chain generation for EEG measurements using compressed sensing. In this system, the signal is masked with pseudo-noise, making the proposed system resistant to cyberattacks. The results of the verification using MATLAB show that a legitimate receiver can reconstruct the EEG signal with NMSE = 0.12 when the magnitude of the inserted pseudo-noise is $200 \mu$ Vrms, whereas it is difficult for an eavesdropper to reconstruct the signal. The accuracy of the watermark estimation on the data processing unit was 100=, even for 24 h of data, indicating that the proposed method is stable for daily use.