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This paper presents a wireless neural recording system featuringenergy-efficient data compression and encryption. An ultra-high efficiency isachieved by leveraging compressed sensing (CS) for simultaneous datacompression and encryption. CS enables sub-Nyquist sampling of neural signalsby taking advantage of its intrinsic sparsity. It simultaneously encrypts thedata with the sampling matrix being the cryptographic key. To share the keyover an insecure wireless channel, we implement an elliptic-curve cryptography(ECC) based key exchanging protocol. The CS operation is executed in acustom-designed IC fabricated in 180nm CMOS technology. Mixed-signal circuitsare designed to optimize the power efficiency of the matrix-vectormultiplication (MVM) of the CS operation. The ECC algorithm is implemented in alow-power Cortex-M0 microcontroller (MCU). To be protected from timing andpower analysis attacks, the implementation avoids possible data-dependentbranches and also employs a randomized ECC initialization. At a compressionratio of 8x, the average correlated coefficient between the reconstructedsignals and the uncompressed signals is 0.973, while the ciphertext-onlyattacks (CoA) achieve no better than 0.054 over 200,000 attacks. The prototypeachieves a 35x power saving compared with conventional implementation inlow-power MCUs. This work demonstrates a promising solution for future chronicneural recording systems with requirements in high energy efficiency andsecurity.

An Energy-efficient Wireless Neural Recording System with Compressed Sensing and Encryption