Activity-Adaptive Architectures for Energy-Efficient Scalable Neural Recording Microsystems: A Review of Current and Future Directions

Wireless transmission of the recorded neural data without exceeding the extremely-limited available power is one of the most significant challenges in developing implantable brain neural interfaces, particularly for systems with higher channel count. Several generic and application-specific data reduction methods have been proposed in the literature with various levels of success in improving energy efficiency while preserving signal integrity. In this paper, we will review different approaches reported and will discuss their advantages and disadvantages. We will also discuss the opportunity that neural ADCs offer recently-reported in realizing an activity-dependent adaptive-resolution fully-dynamic-power neural recording architecture capable of near-loss-less data compression while reducing the required power for both recording and transmission.