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.

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.

This work presents the experimental results of a wavelet transform denoising algorithm (WTDA) that improves the ionoacoustic signal-to-noise ratio (SNR) and proton range measurement precision. Ionoacoustic detectors exploit the ultrasound signal generated by pulsed proton beams in energy absorbers (water or the human body) to localize the energy deposition with sub-millimeter precision, with interesting applications in beam monitoring during oncological hadron therapy treatments. By improving SNR and measurement precision, the WTDA allows significant reduction of the extra dose necessary for beam characterization. To validate the WTDA’s performance, two scenarios are presented. First, the WTDA was applied to the ionoacoustic signals from a 20 MeV proton beam, where it allowed for increasing the SNR by 17 dB and improving measurement precision by a factor of two. Then, the WTDA was applied to the simulated signals from a 200 MeV clinical beam where, compared to state-of-the-art algorithms, it achieved a −80% dose reduction when achieving the same 30 μm precision and a six-fold precision improvement for the same 17 Gy dose deposition.

Ionoacoustic tomography exploits the acoustic signal generated by the fast energy deposition along the path of pulsed particle beams to reconstruct with sub-mm precision the dose deposition, with promising envisioned applications in hadron therapy treatment monitoring. State-of-the-art ionoacoustic detectors mainly rely on single-channel sensors and time-of-flight measurements to provide 1D localization of the maximum dose deposition at the so-called Bragg peak. This work investigates the design challenges of multichannel sensors for ionoacoustic tomography in terms of their ability to accurately reconstruct the dose deposition of a 200 MeV clinical proton beam, highlighting the impact of the number of channels in the array and their directivity. A complete acoustic model of the sensors and environment has been developed and used to find an optimum tradeoff between accuracy, evaluated numerically through the gamma index, and hardware complexity due to higher channel numbers, thus minimizing the system-level power consumption of the detector.

Setting the threshold for a spike detection method is always challenging. The amplitude of the spikes that can be recognized really depends on the noise present in the recording and the threshold must set a good tradeoff between the false detection of noise fluctuations and the smaller spikes. In this work we demonstrate how common approaches to automatic threshold estimation are highly affected by changes in the firing rate of neurons, causing the threshold to become an unreliable yardstick of evaluation between different channels and even within the same recording. For this reason, here we propose a noise estimation method relying on the base 2 logarithm, suitable for the automatic setting of a firing-rate independent threshold for spike detection methods. We compare its performances against other statistical variability indices as the widely used RMS, the mean, and the median of the absolute value of the signal both qualitatively and quantitatively, while varying the firing rate exhibited by the testbench dataset. Our results indicate that while the RMS and the mean are heavily affected by the firing rate, our proposed method performs almost equally to the median-based estimation, but the complexity of the latter poses it as an optimal solution for applications requiring a real-time definition of the threshold.