A signal folding neural amplifier exploiting neural signal statistics - Chen, Yi (2013)
A novel amplifier for neural recording applications that exploits the 1/fn characteristics of neural signals is described in this paper. Comparison and reset circuits are implemented with the core amplifier to fold a large output waveform into a preset range enabling the use of an ADC with less number of bits for the same effective dynamic range. This also reduces the transmission data rate of the recording chip. Both of these features allow power and area savings at the system level. At the receiver, a reconstruction algorithm is applied in the digital domain to recover the amplified signal from the folded waveform. Other features of this proposed amplifier are increased reliability due to removal of pseudo-resistors, less distortion and low-voltage operation. Meaφsurement results from a 65nm CMOS implementation of a prototype are presented.