A Mixed-Signal Multichip Neural Recording Interface With Bandwidth Reduction

We present a multichip structure assembled with a medical-grade stainless-steel microelectrode array intended for neural recordings from multiple channels. The design features a mixed-signal integrated circuit (IC) that handles conditioning, digitization, and time-division multiplexing of neural signals, and a digital IC that provides control, bandwidth reduction, and data communications for telemetry toward a remote host. Bandwidth reduction is achieved through action potential detection and complete capture of waveforms by means of onchip data buffering. The adopted architecture uses high parallelism and low-power building blocks for safety and long-term implantability. Both ICs are fabricated in a CMOS 0.18-mum process and are subsequently mounted on the base of the microelectrode array. The chips are stacked according to a vertical integration approach for better compactness. The presented device integrates 16 channels, and is scalable to hundreds of recording channels. Its performance was validated on a testbench with synthetic neural signals. The proposed interface presents a power consumption of 138 muW per channel, a size of 2.30 mm2, and achieves a bandwidth reduction factor of up to 48 with typical recordings.

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

[2]  K.V. Shenoy,et al.  Power feasibility of implantable digital spike sorting circuits for neural prosthetic systems , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  D. Borton,et al.  A Brain Implantable Microsystem with Hybrid RF/IR Telemetry for Advanced Neuroengineering Applications , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  M. Sawan,et al.  A Microsystem Integration Platform Dedicated to Build Multi-Chip-Neural Interfaces , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  J. Csicsvari,et al.  Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. , 2000, Journal of neurophysiology.

[6]  Mohamad Sawan,et al.  An Ultra-Low-Power Successive-Approximation-Based ADC for Implantable Sensing Devices , 2006, 2006 49th IEEE International Midwest Symposium on Circuits and Systems.

[7]  W.R. Patterson,et al.  Development of a chipscale integrated microelectrode/microelectronic device for brain implantable neuroengineering applications , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[9]  M. Sawan,et al.  Wireless Smart Implants Dedicated to Multichannel Monitoring and Microstimulation , 2005, The IEEE/ACS International Conference on Pervasive Services.

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

[11]  Mohamad Sawan,et al.  Wavelet transforms dedicated to compress recorded ENGs from multichannel implants: comparative architectural study , 2006, 2006 IEEE International Symposium on Circuits and Systems.

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

[13]  P. Tresco,et al.  Response of brain tissue to chronically implanted neural electrodes , 2005, Journal of Neuroscience Methods.

[14]  G. Buzsáki Large-scale recording of neuronal ensembles , 2004, Nature Neuroscience.

[15]  M. Sawan,et al.  An independent-component-analysis-based time-space processor for the identification of neural stimulation sources , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Amir M. Sodagar,et al.  A Fully Integrated Mixed-Signal Neural Processor for Implantable Multichannel Cortical Recording , 2007, IEEE Transactions on Biomedical Engineering.

[17]  R. Olsson,et al.  A three-dimensional neural recording microsystem with implantable data compression circuitry , 2005, ISSCC. 2005 IEEE International Digest of Technical Papers. Solid-State Circuits Conference, 2005..

[18]  Robert Rieger,et al.  Design strategies for multi-channel low-noise recording systems , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[19]  E. Maynard,et al.  A technique to prevent dural adhesions to chronically implanted microelectrode arrays , 2000, Journal of Neuroscience Methods.

[20]  K.P. Koch,et al.  Implantable biomedical microsystems for neural prostheses , 2005, IEEE Engineering in Medicine and Biology Magazine.

[21]  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..

[22]  D. Delpy,et al.  Near-infrared light propagation in an adult head model. I. Modeling of low-level scattering in the cerebrospinal fluid layer. , 2003, Applied optics.

[23]  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.

[24]  D. Spencer,et al.  Invasive EEG in Presurgical Evaluation of Epilepsy , 2008 .

[25]  Patrick D. Wolf,et al.  Evaluation of spike-detection algorithms fora brain-machine interface application , 2004, IEEE Transactions on Biomedical Engineering.

[26]  Matthew Fellows,et al.  On the variability of manual spike sorting , 2004, IEEE Transactions on Biomedical Engineering.

[27]  Patrick D Wolf,et al.  A single-chip signal processing and telemetry engine for an implantable 96-channel neural data acquisition system , 2007, Journal of neural engineering.