Energy-efficient two-stage Compressed Sensing method for implantable neural recordings

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.

[1]  J. Csicsvari,et al.  Intracellular features predicted by extracellular recordings in the hippocampus in vivo. , 2000, Journal of neurophysiology.

[2]  Michael Lindenbaum,et al.  Sequential Karhunen-Loeve basis extraction and its application to images , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[3]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[4]  Awais M. Kamboh,et al.  A Scalable Wavelet Transform VLSI Architecture for Real-Time Signal Processing in High-Density Intra-Cortical Implants , 2007, IEEE Transactions on Circuits and Systems I: Regular Papers.

[5]  Michael Lindenbaum,et al.  Sequential Karhunen-Loeve basis extraction and its application to images , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[6]  Mani B. Srivastava,et al.  Compressive Sensing of Neural Action Potentials Using a Learned Union of Supports , 2011, 2011 International Conference on Body Sensor Networks.

[7]  Guillermo Sapiro,et al.  Learning to Sense Sparse Signals: Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization , 2009, IEEE Transactions on Image Processing.

[8]  Mohamad Sawan,et al.  A Mixed-Signal Multichip Neural Recording Interface With Bandwidth Reduction , 2009, IEEE Transactions on Biomedical Circuits and Systems.

[9]  Jinzhu Jia,et al.  Preconditioning to comply with the Irrepresentable Condition , 2012, 1208.5584.

[10]  Vladimir Stojanovic,et al.  Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors , 2012, IEEE Journal of Solid-State Circuits.

[11]  Refet Firat Yazicioglu,et al.  Reconstruction of neural action potentials using signal dependent sparse representations , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[12]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[13]  Maurits Ortmanns,et al.  Evaluation study of compressed sensing for neural spike recordings , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[15]  Lawrence Carin,et al.  Bayesian Compressive Sensing , 2008, IEEE Transactions on Signal Processing.