Comparison of Sneo-Based Neural Spike Detection Algorithms for Implantable Multi-Transistor Array Biosensors

Abstract

Real-time neural spike detection is an important step in understanding neurological activities and developing brain-silicon interfaces. Recent approaches exploit minimally invasive sensing techniques based on implanted complementary metal-oxide semiconductors (CMOS) multi transistors arrays (MTAs) that limit the damage of the neural tissue and provide high spatial resolution. Unfortunately, MTAs result in low signal-to-noise ratios due to the weak capacitive coupling between the nearby neurons and the sensor and the high noise power coming from the analog front-end. In this paper we investigate the performance achievable by using spike detection algorithms for MTAs, based on some variants of the smoothed non-linear energy operator (SNEO). We show that detection performance benefits from the correlation of the signals detected by the MTA pixels, but degrades when a high firing rate of neurons occurs. We present and compare different approaches and noise estimation techniques for the SNEO, aimed at increasing the detection accuracy at low SNR and making it less dependent on neurons firing rates. The algorithms are tested by using synthetic neural signals obtained with a modified version of NEUROCUBE generator. The proposed approaches outperform the SNEO, showing a more than 20% increase on averaged sensitivity at 0 dB and reduced dependence on the neuronal firing rate.

A Low Power 1024-Channels Spike Detector Using Latch-Based RAM for Real-Time Brain Silicon Interfaces

“…Another interesting work is provided by Zhang et al [20] where the authors used an embedded processor to show the hardware consumption of their solution based on ASO and an adaptive threshold technique which is closer to our proposal. But we provided a simpler and less power greedy threshold technique which was also demonstrated to be less sensitive to the different firing rates of neuron population [19]. In conclusion, our work, providing a good trade-off between detection and complexity, can be considered a good candidate for a real time implantable solution as demonstrates the overall comparison in Table 4.…”

“…where TP, FP and FN represent the number of spikes rightly detected, the number of false spikes (noise detected as putative spike) and the number of missed spikes [19]. Equation (4) represents the relative error (RE) used to provide the goodness of threshold WA whereas the root mean square error (RMSE) is used to estimate the error between the floating point and fixed point method [22].…”

“…It is worth noticing that the effect and analysis of a mean operation over the record matrix has already been studied in our previous work [19] and it is not shown h Briefly, we can assume that if the mechanical properties of the electrodes (pitch, dim sions) are about few tens of µ m it is possible to further reduce the channels to be proces by working on an average signal.…”

“…Following the idea that a spike shows higher amplitude to the noise, we decided to investigate a new approach to estimate the noise considering the limited resource available for a brain machine interface. The method, that we briefly addressed as WA in [19], performs a clipping or truncation of the signal with the aim of discarding the spikes from the distribution under analysis as we want to evaluate the noise distribution. In this way a more precise standard deviation of noise can be obtained.…”

“…Even if the literature provides different threshold techniques, they are often sensitive to noise and spikes variations that worsen the performance of the detector, regardless of their computational requirements. In our previous work we studied and compared different threshold methods identifying in the winsoritation (WA) the more amenable for BMIs [19].…”