Energy efficient EEG acquisition and reconstruction for a Wireless Body Area Network

In Wireless Body Area Networks (WBAN) the energy consumption is dominated by sensing and communication. Previous Compressed Sensing (CS) based solutions to EEG telemonitoring over WBAN's could only reduce the communication cost. In this work, we propose a matrix completion based formulation that can also reduce the energy consumption for sensing. At the heart of the system is an Analog to Information Converter (AIC) implemented in 65 nm CMOS technology. The pseudorandom clock generator enables random under-sampling and subsequent conversion by the 12-bit Successive Approximation Register Analog to Digital Converter (SAR ADC). AIC achieves a sampling rate of 0.5 KS/s, an ENOB 9.54 bits, FOM 187 fj/conv-step and consumes 69.66 nW from 1 V power supply. We test our method with state-of-the-art CS based techniques and find that the reconstruction accuracy of our method is significantly better and that too at considerably less energy consumption. Our method is also tested for post-reconstruction signal classification where it outperforms previous CS based techniques.