Adaptive dual-threshold neural signal compression suitable for implantable recording

This paper presents a digital architecture for neural signal compression using adaptive two-threshold spike detection and a nonlinear discrete wavelet coefficient selection scheme. The circuits and algorithms are described and compared with the state-of-the-art. The proposed 16-channel digital architecture is capable of neural data compression to 0.5% of the original raw data rate while consuming 21μW, with 30-kHz 8-bit sampling, in a 0.8-V 130-nm low-power IBM process.