An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems

A power and area effcient hardware encoding system tailored for wireless implantable applications is presented. Constant medical monitoring allowed by implantable devices is the most relevant alternative to current bulky monitoring systems, which, in case of severe mental diseases, require heavy surgery and long term hospitalization periods. In this work, the circuit design and the signal processing algorithm dovetail in order to allow real-time neuronal signal monitoring. Two main features must be met on the circuit level to facilitate the acceptance of the implant from the human body: small area and low power consumption. The presented work proposes a new compression scheme based on the Learning-Based Compressive Subsampling approach, which allows an area reduction with respect to recent published works, while allowing high signal reconstruction quality within low power requirements. The proposed method implements on-the-fly compression coeffcients generation, which does not require large static memories. This new fully digital architecture handles the data compression of each individual neuronal acquisition channel with an area of 200 x 190μ in 0:18 μm CMOS technology, and a power dissipation of only 1:15μW.