Evaluation of In Vivo Spike Detection Algorithms for Implantable MTA Brain—Silicon Interfaces

Mattia Tambaro,Elia Arturo Vallicelli,Gerardo Saggese,Stefano Vassanelli,Antonio Strollo,Andrea Baschirotto

doi:10.3390/jlpea10030026

Abstract

This work presents a comparison between different neural spike algorithms to find the optimum for in vivo implanted EOSFET (electrolyte–oxide-semiconductor field effect transistor) sensors. EOSFET arrays are planar sensors capable of sensing the electrical activity of nearby neuron populations in both in vitro cultures and in vivo experiments. They are characterized by a high cell-like resolution and low invasiveness compared to probes with passive electrodes, but exhibit a higher noise power that requires ad hoc spike detection algorithms to detect relevant biological activity. Algorithms for implanted devices require good detection accuracy performance and low power consumption due to the limited power budget of implanted devices. A figure of merit (FoM) based on accuracy and resource consumption is presented and used to compare different algorithms present in the literature, such as the smoothed nonlinear energy operator and correlation-based algorithms. A multi transistor array (MTA) sensor of 7 honeycomb pixels of a 30 μm2 area is simulated, generating a signal with Neurocube. This signal is then used to validate the algorithms’ performances. The results allow us to numerically determine which is the most efficient algorithm in the case of power constraint in implantable devices and to characterize its performance in terms of accuracy and resource usage.

Highlights

Intracortical brain computer interfaces have recently seen an increase in research and usage, with scientists from various fields showing increasing interest in their fabrication and adoption
EOSFETs are composed of a standard complementary metal-oxide semiconductor (CMOS) transistor [2] combined with a biocompatible oxide covering the metal gate [3,4], which creates a capacitive coupling between the neuron and the electronics [5]
This paper aims to compare different spike detection algorithms to understand which one can be suitable to be implemented in an implantable device relying on two main criteria: the possibility to be implemented in real-time with a low resource footprint and to be adapted to exploit the spatial correlation of the high density pixel matrix of an multi transistor array (MTA) sensor

Summary

Introduction

Intracortical brain computer interfaces have recently seen an increase in research and usage, with scientists from various fields showing increasing interest in their fabrication and adoption These neural sensors can be used to monitor the extracellular electrical activity of neurons in both shortand long-lasting in vivo implants. EOSFETs are composed of a standard complementary metal-oxide semiconductor (CMOS) transistor [2] combined with a biocompatible oxide covering the metal gate [3,4], which creates a capacitive coupling between the neuron and the electronics [5] This approach is invasive but tissue-compatible [6], as there is a distance between neurons and the sensor surface that helps limit damage to cells and allows for lasting implants. Such devices usually provide a limited signal-to-noise ratio (SNR, 3 to 6 dB) per channel compared to standard passive electrodes [7], but

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