A Low-Power Programmable Neural Spike Detection Channel With Embedded Calibration and Data Compression

This paper reports a programmable 400 μm pitch neural spike recording channel, fabricated in a 130 nm standard CMOS technology, which implements amplification, filtering, digitization, analog spike detection plus feature extraction, and self-calibration functionalities. It can operate in two different output modes: 1) signal tracking, in which the neural signal is sampled and transmitted as raw data; and 2) feature extraction, in which the spikes of the neural signal are detected and encoded by piece-wise linear curves. Additionally, the channel offers a foreground calibration procedure in which the amplification gain and the passband of the embedded filter can be self-adjusted. The amplification stage obtains a noise efficiency factor of 2.16 and an input referred noise of 2.84 μVrms over a nominal bandwidth of 167 Hz-6.9 kHz. The channel includes a reconfigurable 8-bit analog-to-digital converter combined with a 3-bit controlled programmable gain amplifier for adjusting the input signal to the full scale range of the converter. This combined block achieves an overall energy consumption per conversion of 102 fJ at 90 kS/s. The energy consumed by the circuit elements which are strictly related to the digitization process is 14.12 fJ at the same conversion rate. The complete channel consumes 2.8 μW at 1.2 V voltage supply when operated in the signal tracking mode, and 3.1 μW when the feature extraction mode is enabled.

[1]  Ángel Rodríguez-Vázquez,et al.  Accurate Settling-Time Modeling and Design Procedures for Two-Stage Miller-Compensated Amplifiers for Switched-Capacitor Circuits , 2009, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  R.R. Harrison,et al.  A Low-Power Integrated Circuit for a Wireless 100-Electrode Neural Recording System , 2006, IEEE Journal of Solid-State Circuits.

[3]  Ángel Rodríguez-Vázquez,et al.  A power efficient neural spike recording channel with data bandwidth reduction , 2011, 2011 IEEE International Symposium of Circuits and Systems (ISCAS).

[4]  J. Mason Andrew,et al.  On-chip feature extraction for spike sorting in high density implantable neural recording systems , 2010, 2010 Biomedical Circuits and Systems Conference (BioCAS).

[5]  R. R. Harrison,et al.  A low-power low-noise CMOS amplifier for neural recording applications , 2003, IEEE J. Solid State Circuits.

[6]  Yusuf Leblebici,et al.  Energy Efficient Low-Noise Neural Recording Amplifier With Enhanced Noise Efficiency Factor , 2011, IEEE Transactions on Biomedical Circuits and Systems.

[7]  Yong Lian,et al.  A 1-V 450-nW Fully Integrated Programmable Biomedical Sensor Interface Chip , 2009, IEEE Journal of Solid-State Circuits.

[8]  A.P. Chandrakasan,et al.  An Ultra Low Energy 12-bit Rate-Resolution Scalable SAR ADC for Wireless Sensor Nodes , 2007, IEEE Journal of Solid-State Circuits.

[9]  Ying Yao,et al.  An Implantable 64-Channel Wireless Microsystem for Single-Unit Neural Recording , 2009, IEEE Journal of Solid-State Circuits.

[10]  Rizwan Bashirullah,et al.  Neural cache: A low-power online digital spike-sorting architecture , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Fan Zhang,et al.  A 500µW neural tag with 2µVrms AFE and frequency-multiplying MICS/ISM FSK transmitter , 2009, 2009 IEEE International Solid-State Circuits Conference - Digest of Technical Papers.

[12]  Maysam Ghovanloo,et al.  An Inductively Powered Scalable 32-Channel Wireless Neural Recording System-on-a-Chip for Neuroscience Applications , 2010, IEEE Transactions on Biomedical Circuits and Systems.

[13]  Rahul Sarpeshkar,et al.  An Energy-Efficient Micropower Neural Recording Amplifier , 2007, IEEE Transactions on Biomedical Circuits and Systems.

[14]  C Gabriel,et al.  The dielectric properties of biological tissues: I. Literature survey. , 1996, Physics in medicine and biology.

[15]  Pedram Mohseni,et al.  A fully integrated neural recording amplifier with DC input stabilization , 2004, IEEE Transactions on Biomedical Engineering.

[16]  Ran Ginosar,et al.  An Integrated System for Multichannel Neuronal Recording With Spike/LFP Separation, Integrated A/D Conversion and Threshold Detection , 2007, IEEE Trans. Biomed. Eng..

[17]  Wei Zhao,et al.  A low-noise integrated bioamplifier with active DC offset suppression , 2009, 2009 IEEE Biomedical Circuits and Systems Conference.

[18]  W. Liu,et al.  A 128-Channel 6 mW Wireless Neural Recording IC With Spike Feature Extraction and UWB Transmitter , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  W.M.C. Sansen,et al.  A micropower low-noise monolithic instrumentation amplifier for medical purposes , 1987 .

[20]  Franco Maloberti,et al.  A 9.4-ENOB 1V 3.8μW 100kS/s SAR ADC with Time-Domain Comparator , 2008, 2008 IEEE International Solid-State Circuits Conference - Digest of Technical Papers.

[21]  Jordi Parramon,et al.  A Micropower Low-Noise Neural Recording Front-End Circuit for Epileptic Seizure Detection , 2011, IEEE Journal of Solid-State Circuits.

[22]  S. Gambini,et al.  Low-Power Successive Approximation Converter With 0.5 V Supply in 90 nm CMOS , 2007, IEEE Journal of Solid-State Circuits.

[23]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.

[24]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[25]  K. Wise,et al.  A three-dimensional microelectrode array for chronic neural recording , 1994, IEEE Transactions on Biomedical Engineering.

[26]  Andrew B. Schwartz,et al.  Brain-Controlled Interfaces: Movement Restoration with Neural Prosthetics , 2006, Neuron.

[27]  Ángel Rodríguez-Vázquez,et al.  Transistor-Level Synthesis of Pipeline Analog-to-Digital Converters Using a Design-Space Reduction Algorithm , 2011, IEEE Transactions on Circuits and Systems I: Regular Papers.

[28]  Randall D. Beer,et al.  An MDAC synapse for analog neural networks , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

[29]  R. Quian Quiroga,et al.  Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering , 2004, Neural Computation.

[30]  Manuel Delgado-Restituto,et al.  A review of low-noise amplifiers for neural applications , 2010, 2010 2nd Circuits and Systems for Medical and Environmental Applications Workshop (CASME).

[31]  Yong Lian,et al.  A 1-V 60-µW 16-channel interface chip for implantable neural recording , 2009, 2009 IEEE Custom Integrated Circuits Conference.

[32]  Manuel Delgado-Restituto,et al.  A self-calibration circuit for a neural spike recording channel , 2011, 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[33]  M. Delgado-Restituto,et al.  A low-power reconfigurable ADC for biomedical sensor interfaces , 2009, 2009 IEEE Biomedical Circuits and Systems Conference.

[34]  Karim Abdelhalim,et al.  The 128-Channel Fully Differential Digital Integrated Neural Recording and Stimulation Interface , 2010, IEEE Transactions on Biomedical Circuits and Systems.

[35]  Craig T. Nordhausen,et al.  Single unit recording capabilities of a 100 microelectrode array , 1996, Brain Research.

[36]  Mohamad Sawan,et al.  A Low-Power Integrated Bioamplifier With Active Low-Frequency Suppression , 2007, IEEE Transactions on Biomedical Circuits and Systems.

[37]  Kristofer S. J. Pister,et al.  An ultralow-energy ADC for Smart Dust , 2003, IEEE J. Solid State Circuits.

[38]  Manuel Delgado-Restituto,et al.  A reconfigurable neural spike recording channel with feature extraction capabilities , 2010, 2010 Biomedical Circuits and Systems Conference (BioCAS).

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

[40]  Dejan Markovic,et al.  Comparison of spike-sorting algorithms for future hardware implementation , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.