Energy efficient acquisition and reconstruction of EEG signals

In Wireless Body Area Networks (WBAN) the energy consumption is dominated by sensing and communication. Previous Compressed Sensing (CS) based solutions to EEG tele-monitoring 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. We test our method with state-of-the-art CS based techniques and find that the reconstruction accuracy from 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. At the heart of the system is an Analog to Information Converter (AIC) implemented in 65nm 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 sample rate of 0.5 KS/s, an ENOB 9.54 bits, and consumes 108 nW from 1 V power supply.