For in-vivo neuroscience experiments, implantable neural recording devices have been widely used to capture neural activity. With high acquisition rate, these devices require efficient on-chip compression methods to reduce power consumption for the subsequent wireless transmission. Recently, Compressed Sensing (CS) approaches have shown great potentials, but there exists the tradeoff between the complexity of the sensing circuit and its compression performance. To address this challenge, we proposed a two-stage CS method, including an on-chip sensing using random Bernoulli Matrix S and an off-chip sensing using Puffer transformation P. Our approach allows a simple circuit design and improves the reconstruction performance with the off-chip sensing. Moreover, we proposed to use measureed data as the sparsifying dictionary D. It delivers comparable reconstruction performance to the signal dependent dictionary and outperforms the standard basis. It also allows both D and P to be updated incrementally with reduced complexity. Experiments on simulation and real datasets show that the proposed approach can yield an average SNDR gain of more than 2 dB over other CS approaches.
展开▼
