IEEE Transactions on Neural Systems and Rehabilitation Engineering | 2019
Compact and Low-Power Neural Spike Compression Using Undercomplete Autoencoders
Abstract
Implantable microsystems that collect and transmit neural data are becoming very useful entities in the field of neuroscience. Limited by high data rates, on-chip compression is often required to transmit the recorded data without causing power dissipation at levels that would damage sensitive brain tissue. This paper presents a data compression system designed for brain–computer interfaces (BCIs) based on undercomplete autoencoders. To the best of our knowledge, the proposed system is the first to achieve an average spike reconstruction quality of 14-dB signal-to-noise-and-distortion ratio (SNDR) at a <inline-formula> <tex-math notation= LaTeX >$32\\times $ </tex-math></inline-formula> compression ratio (CR), 18-dB SNDR at a <inline-formula> <tex-math notation= LaTeX >$16\\times $ </tex-math></inline-formula> CR, 22-dB SNDR at an <inline-formula> <tex-math notation= LaTeX >$8\\times $ </tex-math></inline-formula> CR, and 35-dB SNDR at a <inline-formula> <tex-math notation= LaTeX >$4\\times $ </tex-math></inline-formula> CR of neural spikes. The spike detection and autoencoder-based compression modules are designed and implemented in a standard 45-nm CMOS process. The post-synthesis simulation results report that the compression module consumes between 1.4 and 222.5 <inline-formula> <tex-math notation= LaTeX >$\\mu \\text{W}$ </tex-math></inline-formula> of power per channel and takes between 0.018 and 0.082mm<sup>2</sup> of silicon area, depending on the desired CR and number of channels.