FPGA Design Integration of a 32-Microelectrodes Low-Latency Spike Detector in a Commercial System for Intracortical Recordings

Abstract

Numerous experiments require low latencies in the detection and processing of the neural brain activity to be feasible, in the order of a few milliseconds from action to reaction. In this paper, a design for sub-millisecond detection and communication of the spiking activity detected by an array of 32 intracortical microelectrodes is presented, exploiting the real-time processing provided by Field Programmable Gate Array (FPGA). The design is embedded in the commercially available RHS stimulation/recording controller from Intan Technologies, that allows recording intracortical signals and performing IntraCortical MicroStimulation (ICMS). The Spike Detector (SD) is based on the Smoothed Nonlinear Energy Operator (SNEO) and includes a novel approach to estimate an RMS-based firing-rate-independent threshold, that can be tuned to fine detect both the single Action Potential (AP) and Multi Unit Activity (MUA). A low-latency SD together with the ICMS capability, creates a powerful tool for Brain-Computer-Interface (BCI) closed-loop experiments relying on the neuronal activity-dependent stimulation. The design also includes: A third order Butterworth high-pass IIR filter and a Savitzky-Golay polynomial fitting; a privileged fast USB connection to stream the detected spikes to a host computer and a sub-milliseconds latency Universal Asynchronous Receiver-Transmitter (UART) protocol communication to send detections and receive ICMS triggers. The source code and the instruction of the project can be found on GitHub.

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

“…Neural sensors should be able to detect voltage variations on a large scale (Saggese et al, 2021 ). Each electrode records by default extracellular de- and hyperpolarizations in the close vicinity of its nano-to-micrometer-wide tip (to a distance of about 140 μm in the case of a single wire electrode), and given the propagating nature of action potentials, these deflections would not only be APs assigned to a particular neuron, or single unit activity (SUA) but rather the spatio-temporal summation of a neural population in the close proximity, multi-unit activities (MUAs), and local field potentials (Hong and Lieber, 2019 ; Tambaro et al, 2021 ). Most spike sorting techniques discard local field potentials by simply high pass filtering data and concentrate on the spatial and temporal contexts of signal propagation (Abbott et al, 2020 ), although invasive brain-machine interface (BMI) systems could also possibly profit from this frequency range (Hammad et al, 2016 ).…”

“…Besides threshold crossing, plenty of algorithms enable action potential detection. Smoothed or common non-linear energy operators may be capable of sub-millisecond on-chip spike detection (Malik et al, 2016 ; Schaffer et al, 2017 ; Tambaro et al, 2021 ). Signal-to-noise ratio can be further augmented by amplitude-slope operators (Zhang and Constandinou, 2021b ).…”

SpikeOnChip : A Custom Embedded Platform for Neuronal Activity Recording and Analysis

“…Its key specifications are the integration of the following important features: it should (1) use cost-effective off the shelf parts and allow autonomous battery operation; (2) be fully customizable to the needs of the experiments; (3) allow further improvements, such as increasing the number of recording channels, and integrate more advanced data processing and analysis; and (4) allow the deployment of each of the different data processing blocks into the part of the system that is more suitable for achieving optimal performance. On-chip spike detection has been explored on FPGA or ASIC platforms [19]- [23], but solutions rely on custom boards or ASICs and do not integrate on-line network activity analysis, e.g., in frequency domain. The authors of [24] present a 32-channel solution with off the shelf parts but the platform is limited in FPGA size and processing power making further developments difficult.…”

Classification of Whisker Deflections From Evoked Responses in the Somatosensory Barrel Cortex With Spiking Neural Networks

“…An additional fundamental aspect when dealing with evoked responses is their dependence on basal brain activity (Petersen et al, 2003 ), which may pose different challenges for LFPs or spikes. Solutions for online-processing spikes and LFPs that are or may become suitable for brain implants have been proposed based on analog and digital processors (Tambaro et al, 2021 ). Only a minority have explored neuromorphic architectures and a few of them spiking neural networks (SNNs) (Boi et al, 2016 ; Werner et al, 2016 ; Mukhopadhyay et al, 2021 ; Sharifshazileh et al, 2021 ).…”