Active C4 electrodes for local field potential recording applications

Extracellular neural recording, with multi-electrode arrays (MEAs), is a powerful method used to study neural function at the network level. However, in a high density array, it can be costly and time consuming to integrate the active circuit with the expensive electrodes. In this paper, we present a 4 mm × 4 mm neural recording integrated circuit (IC) chip, utilizing IBM C4 bumps as recording electrodes, which enable a seamless active chip and electrode integration. The IC chip was designed and fabricated in a 0.13 μm BiCMOS process for both in vitro and in vivo applications. It has an input-referred noise of 4.6 μV rms for the bandwidth of 10 Hz to 10 kHz and a power dissipation of 11.25 mW at 2.5 V, or 43.9 μW per input channel. This prototype is scalable for implementing larger number and higher density electrode arrays. To validate the functionality of the chip, electrical testing results and acute in vivo recordings from a rat barrel cortex are presented.R01 NS072385 - NINDS NIH HHS; 1R01 NS072385 - NINDS NIH HH

Design and Optimization of a Low DC Offset in Implanted System for ENG Recording Based on Velocity Selectivity Method

The major target of this paper is the design of advance signal processing system based on minimized length of bits required for digital-to-analogy converter (DAC) for velocity selectivity recording (VSR) approach. The main application of this device is peripheral nerves recording (electroneurogram-ENG) by exploring a spectral analysis for the propagation of neural activities in the velocity domain recording using VSR in implantable application. This research adapted a flexible, compact, andnbspenergynbspefficient dc offset removal circuit. An optimization design has been used based on best possible process involving linearity and area is thus suggested. The system process acquired using this approach were characterized as having a 10-bit signal processing for DAC resolution, with 1.4 mA rms output current, with minimum size around 0.02 mm2nbspof chip area, using FPGA board as prototype design. This paper also explores the design temperature vibration in online recording minimization the output DC offset decrease the heat emission which is significantly for long term implementation applications. This study proposed an analysis circuit configuration demonstrate that this approach could achieve a small DC offset error, with small size required

A cell-electrode interface noise model for high-density microelectrode arrays

A cell-electrode interface noise model is developed which is dedicated to enable the co-simulation of the cell-electrode electrical characteristics, along with the electronics of novel CMOS-based MEA. The electrode noise is investigated for Pt and Pt black electrodes. It is shown that the electrode noise can be the dominant noise source in the full system. Moreover, Pt black electrodes benefit from up to 5 µVrms decrease of the electrode output noise, for small electrodes. Furthermore, the cell-electrode interface noise spectral density is shown to be 10 dB to 20 dB larger at 1 kHz when a cell is lying on top of the electrode. This increase depends on the neural cell adhesion on the MEA surface

On the way to large-scale and high-resolution brain-chip interfacing

Brain-chip-interfaces (BCHIs) are hybrid entities where chips and nerve cells establish a close physical interaction allowing the transfer of information in one or both directions. Typical examples are represented by multi-site-recording chips interfaced to cultured neurons, cultured/acute brain slices, or implanted “in vivo”. This paper provides an overview on recent achievements in our laboratory in the field of BCHIs leading to enhancement of signals transmission from nerve cells to chip or from chip to nerve cells with an emphasis on in vivo interfacing, either in terms of signal-to-noise ratio or of spatiotemporal resolution. Oxide-insulated chips featuring large-scale and high-resolution arrays of stimulation and recording elements are presented as a promising technology for high spatiotemporal resolution interfacing, as recently demonstrated by recordings obtained from hippocampal slices and brain cortex in implanted animals. Finally, we report on an automated tool for processing and analysis of acquired signals by BCHIs

A 4.8-μVrms-Noise CMOS-Microelectrode Array With Density-Scalable Active Readout Pixels via Disaggregated Differential Amplifier Implementation

We demonstrate a 4.8-μVrms noise microelectrode array (MEA) based on the complementary-metal-oxide-semiconductor active-pixel-sensors readout technique with disaggregated differential amplifier implementation. The circuit elements of the differential amplifier are divided into a readout pixel, a reference pixel, and a column circuit. This disaggregation contributes to the small area of the readout pixel, which is less than 81 μm2. We observed neuron signals around 100 μV with 432 electrodes in a fabricated prototype chip. The implementation has technological feasibility of up to 12-μm-pitch electrode density and 6,912 readout channels for high-spatial resolution mapping of neuron network activity

Large-scale, high-resolution electrophysiological imaging of field potentials in brain slices with microelectronic multielectrode arrays

Multielectrode arrays (MEAs) are extensively used for electrophysiological studies on brain slices, but the spatial resolution and field of recording of conventional arrays are limited by the low number of electrodes available. Here, we present a large-scale array recording simultaneously from 4096 electrodes used to study propagating spontaneous and evoked network activity in acute murine cortico-hippocampal brain slices at unprecedented spatial and temporal resolution. We demonstrate that multiple chemically induced epileptiform episodes in the mouse cortex and hippocampus can be classified according to their spatio-temporal dynamics. Additionally, the large-scale and high-density features of our recording system enable the topological localization and quantification of the effects of antiepileptic drugs in local neuronal microcircuits, based on the distinct field potential propagation patterns. This novel high-resolution approach paves the way to detailed electrophysiological studies in brain circuits spanning spatial scales from single neurons up to the entire slice network