A Sub- $\mu$ W Reconfigurable Front-End for Invasive Neural Recording That Exploits the Spectral Characteristics of the Wideband Neural Signal

This paper presents a sub-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> ac-coupled reconfigurable front-end for invasive wideband neural signal recording. The proposed topology embeds filtering capabilities enabling the selection of different frequency bands inside the neural signal spectrum. Power consumption is optimized by defining specific noise targets for each sub-band. These targets take into account the spectral characteristics of wideband neural signals: local field potentials (LFP) exhibit <inline-formula> <tex-math notation="LaTeX">$\mathrm {1/f^{x}}$ </tex-math></inline-formula> magnitude scaling while action potentials (AP) show uniform magnitude across frequency. Additionally, noise targets also consider electrode noise and the spectral distribution of noise sources in the circuit. An experimentally verified prototype designed in a standard 180 nm CMOS process draws 815 nW from a 1 V supply. The front-end is able to select among four different frequency bands (modes) up to 5 kHz. The measured input-referred spot-noise at 500 Hz in the LFP mode (1 Hz - 700 Hz) is <inline-formula> <tex-math notation="LaTeX">$55~nV/\sqrt {Hz}$ </tex-math></inline-formula> while the integrated noise in the AP mode (200 Hz - 5 kHz) is <inline-formula> <tex-math notation="LaTeX">$4.1~\mu Vrms$ </tex-math></inline-formula>. The proposed front-end achieves sub-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> operation without penalizing other specifications such as input swing, common-mode or power-supply rejection ratios. It reduces the power consumption of neural front-ends with spectral selectivity by <inline-formula> <tex-math notation="LaTeX">$6.1\times $ </tex-math></inline-formula> and, compared with conventional wideband front-ends, it obtains a reduction of <inline-formula> <tex-math notation="LaTeX">$2.5\times $ </tex-math></inline-formula>.

[1]  Timothy G. Constandinou,et al.  A 64-Channel Versatile Neural Recording SoC With Activity-Dependent Data Throughput , 2017, IEEE Transactions on Biomedical Circuits and Systems.

[2]  Nicholas V. Annetta,et al.  Restoring cortical control of functional movement in a human with quadriplegia , 2016, Nature.

[3]  T. Seese,et al.  Characterization of tissue morphology, angiogenesis, and temperature in the adaptive response of muscle tissue to chronic heating. , 1998, Laboratory investigation; a journal of technical methods and pathology.

[4]  Yiannos Manoli,et al.  A 0.01 mm2 fully-differential 2-stage amplifier with reference-free CMFB using an architecture-switching-scheme for bandwidth variation , 2015, ESSCIRC Conference 2015 - 41st European Solid-State Circuits Conference (ESSCIRC).

[5]  R. Andersen,et al.  Cortical Local Field Potential Encodes Movement Intentions in the Posterior Parietal Cortex , 2005, Neuron.

[6]  David J. Warren,et al.  Comparative characterization of in vivo and in vitro noise of the SIROF Utah electrode array , 2017, 2017 IEEE SENSORS.

[7]  Jose M. Carmena,et al.  Exploiting the 1/f structure of neural signals for the design of integrated neural amplifiers , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Brian P. Otis,et al.  Exploiting Electrocorticographic Spectral Characteristics for Optimized Signal Chain Design: A 1.08 W Analog Front End With Reduced ADC Resolution Requirements , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[9]  Pui-In Mak,et al.  An Integrated Circuit for Simultaneous Extracellular Electrophysiology Recording and Optogenetic Neural Manipulation , 2017, IEEE Transactions on Biomedical Engineering.

[10]  Timothy H. Lucas,et al.  Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control , 2017, IEEE Transactions on Biomedical Circuits and Systems.

[11]  N. Ramsey,et al.  Fully Implanted Brain-Computer Interface in a Locked-In Patient with ALS. , 2016, The New England journal of medicine.

[12]  G. Buzsáki,et al.  Neuronal Oscillations in Cortical Networks , 2004, Science.

[13]  Srinjoy Mitra,et al.  A Neural Probe With Up to 966 Electrodes and Up to 384 Configurable Channels in 0.13 $\mu$m SOI CMOS , 2017, IEEE Transactions on Biomedical Circuits and Systems.

[14]  Matias J. Ison,et al.  Realistic simulation of extracellular recordings , 2009, Journal of Neuroscience Methods.

[15]  Zoran Nenadic,et al.  CMOS Ultralow Power Brain Signal Acquisition Front-Ends: Design and Human Testing , 2017, IEEE Transactions on Biomedical Circuits and Systems.

[16]  Jian Xu,et al.  A Frequency Shaping Neural Recorder With 3 pF Input Capacitance and 11 Plus 4.5 Bits Dynamic Range , 2014, IEEE Transactions on Biomedical Circuits and Systems.

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

[18]  Karim Abdelhalim,et al.  Battery-less Tri-band-Radio Neuro-monitor and Responsive Neurostimulator for Diagnostics and Treatment of Neurological Disorders , 2016, IEEE Journal of Solid-State Circuits.

[19]  Ángel Rodríguez-Vázquez,et al.  A Sub-µW Reconfigurable Front-End for Invasive Neural Recording , 2019, 2019 IEEE 10th Latin American Symposium on Circuits & Systems (LASCAS).

[20]  Roman Genov,et al.  Artifact-Tolerant Opamp-Less Delta-Modulated Bidirectional Neuro-Interface , 2018, 2018 IEEE Symposium on VLSI Circuits.

[21]  Christopher M. Twigg,et al.  A Fully Reconfigurable Low-Noise Biopotential Sensing Amplifier With 1.96 Noise Efficiency Factor , 2014, IEEE Transactions on Biomedical Circuits and Systems.

[22]  Jan M. Rabaey,et al.  Reliable Next-Generation Cortical Interfaces for Chronic Brain–Machine Interfaces and Neuroscience , 2017, Proceedings of the IEEE.

[23]  Yuanjin Zheng,et al.  A 0.45 V 100-Channel Neural-Recording IC With Sub-µW/Channel Consumption in 0.18 µm CMOS , 2013, IEEE Trans. Biomed. Circuits Syst..

[24]  C. Koch,et al.  Neuronal Shot Noise and Brownian 1/f2 Behavior in the Local Field Potential , 2008, PloS one.

[25]  Shuang Song,et al.  A 430nW 64nV/vHz current-reuse telescopic amplifier for neural recording applications , 2013, 2013 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[26]  Supratim Ray,et al.  Effect of Reference Scheme on Power and Phase of the Local Field Potential , 2016, Neural Computation.

[27]  Reid R. Harrison,et al.  The Design of Integrated Circuits to Observe Brain Activity , 2008, Proceedings of the IEEE.

[28]  Rahul Sarpeshkar,et al.  A Low-Power Wide-Linear-Range Transconductance Amplifier , 1997 .

[29]  Francis R. Willett,et al.  High performance communication by people with paralysis using an intracortical brain-computer interface , 2017, eLife.

[30]  B. Litt,et al.  High-frequency oscillations in human temporal lobe: simultaneous microwire and clinical macroelectrode recordings. , 2008, Brain : a journal of neurology.

[31]  Jeffrey G. Ojemann,et al.  Power-Law Scaling in the Brain Surface Electric Potential , 2009, PLoS Comput. Biol..

[32]  Maysam Ghovanloo,et al.  An Adaptive Averaging Low Noise Front-End for Central and Peripheral Nerve Recording , 2018, IEEE Transactions on Circuits and Systems II: Express Briefs.

[33]  Reid R. Harrison,et al.  A low-power, low-noise CMOS amplifier for neural recording applications , 2002, 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353).

[34]  Jean Gotman,et al.  High-frequency (80–500Hz) oscillations and epileptogenesis in temporal lobe epilepsy , 2011, Neurobiology of Disease.

[35]  Benoit Gosselin,et al.  A Low-Power Current-Reuse Analog Front-End for High-Density Neural Recording Implants , 2018, IEEE Transactions on Biomedical Circuits and Systems.

[36]  K. H. Britten,et al.  Power spectrum analysis of bursting cells in area MT in the behaving monkey , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[37]  David J. Warren,et al.  Impedance and Noise Characterizations of Utah and Microwire Electrode Arrays , 2018, IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology.

[38]  Hariprasad Chandrakumar,et al.  A High Dynamic-Range Neural Recording Chopper Amplifier for Simultaneous Neural Recording and Stimulation , 2017, IEEE Journal of Solid-State Circuits.

[39]  Minkyu Je,et al.  A Sub-µW/Ch Analog Front-End for Δ-Neural Recording With Spike-Driven Data Compression , 2019, IEEE Trans. Biomed. Circuits Syst..

[40]  Chung-Yu Wu,et al.  An 8-channel power-efficient time-constant-enhanced analog front-end amplifier for neural signal acquisition , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).

[41]  Fan Zhang,et al.  Design of Ultra-Low Power Biopotential Amplifiers for Biosignal Acquisition Applications , 2012, IEEE Transactions on Biomedical Circuits and Systems.

[42]  Hao Gao,et al.  A 0.20 mm2 3 nW Signal Acquisition IC for Miniature Sensor Nodes in 65 nm CMOS , 2016, IEEE J. Solid State Circuits.

[43]  Yong Ping Xu,et al.  A Low-Power, High CMRR Neural Amplifier System Employing CMOS Inverter-Based OTAs With CMFB Through Supply Rails , 2016, IEEE Journal of Solid-State Circuits.

[44]  Agrita Dubey,et al.  Cortical Electrocorticogram (ECoG) Is a Local Signal , 2019, The Journal of Neuroscience.

[45]  Rahul Sarpeshkar.  Universal Principles for Ultra Low Power and Energy Efficient Design , 2012, IEEE Transactions on Circuits and Systems II: Express Briefs.

[46]  Fernando Silveira,et al.  Current-Efficient Preamplifier Architecture for CMRR Sensitive Neural Recording Applications , 2018, IEEE Transactions on Biomedical Circuits and Systems.

[47]  Timothy Denison,et al.  Integrated circuit amplifiers for multi-electrode intracortical recording , 2009, Journal of neural engineering.