HiCCE-128: An open hardware FMC module for High-Channel Count Electrophysiology

Electrophysiology has recently evolved into an interactive, high-throughput endeavor. Recording from dozens to hundreds of electrodes is today routine; novel means of manipulating the system in real time, through electrical stimulation, optogenetics or sensory manipulation are allowing us to decipher neural circuit function at an unparalleled rate. To contribute to the wide dissemination of such techniques, we present an open hardware project, High-Channel Count Electrophysiology (HiCCE), aiming to produce low-cost, high-channel count (≥128 channels) electrophysiology instrumentation capable of fast data acquisition rates, real-time processing and feedback capabilities. Our design is centered on an open standard, FPGA Mezzanine Card (FMC), which permits a varied choice of FPGA carrier architectures suited to different laboratory experimental needs. The HiCCE-128, a low-cost highperformance 128-channel data acquisition board for small voltage signals, is being introduced. It is a FMC module that can be operated from any FPGA carrier conforming to the FMC/VITA57 standard. This specialized board with a low input referred noise of about 3 μV is capable of acquiring 128 channels simultaneously at 31.25 kS/s per channel with 16 effective bits of resolution. We present the global architecture and some preliminary measurement to illustrate its potential for electrophysiological and medical applications.

[1]  Xiao Yun,et al.  Low-Power High-Resolution 32-channel Neural Recording System , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Silverio Bolognani,et al.  Potentials and pitfalls of FPGA application in inverter drives - a case study , 2003, IEEE International Conference on Industrial Technology, 2003.

[3]  Alexander Shapiro,et al.  A Block-Based Open Source Approach for a Reconfigurable Virtual Instrumentation Platform Using FPGA Technology , 2006, 2006 IEEE International Conference on Reconfigurable Computing and FPGA's (ReConFig 2006).

[4]  Jorge Pomares,et al.  A Survey on FPGA-Based Sensor Systems: Towards Intelligent and Reconfigurable Low-Power Sensors for Computer Vision, Control and Signal Processing , 2014, Sensors.

[5]  E. J. Chichilnisky Responses of complete neural populations in primate retina to naturalistic stimuli , 2014 .

[6]  N Jeremy Hill,et al.  Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping. , 2012, Journal of visualized experiments : JoVE.

[7]  Phil Kinsman Design of a Rapid Prototyping Platform for Applications in Physiological Signal Processing , 2010 .

[8]  Tao Chen,et al.  A design of multi-frequency and multi-channel weak signal data acquisition system based on DSP and FPGA , 2010, 2010 The 2nd Conference on Environmental Science and Information Application Technology.

[9]  Jakob Voigts,et al.  Neural ensemble communities: open-source approaches to hardware for large-scale electrophysiology , 2015, Current Opinion in Neurobiology.

[10]  S. A. Shamma,et al.  MANTA—an open-source, high density electrophysiology recording suite for MATLAB , 2013, Front. Neural Circuits.

[11]  Andres Cicuttin,et al.  Building an Evolvable Low-Cost HW/SW Educational Platform--Application to Virtual Instrumentation , 2007, 2007 IEEE International Conference on Microelectronic Systems Education (MSE'07).

[12]  Naotaka Fujii,et al.  Cortical network architecture for context processing in primate brain , 2015, eLife.

[13]  Dmitriy Aronov,et al.  Engagement of Neural Circuits Underlying 2D Spatial Navigation in a Rodent Virtual Reality System , 2014, Neuron.