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.

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.

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.

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