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.

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Clinical ions beams for cancer treatment provide maximum energy deposition (Bragg peak (BP)) at the end of their range and practically no dose behind. This enables a more efficient therapeutic option comparing with classical photon-based radiotherapy where maximum energy is deposited at the body interface. Obviously, minimum-error BP detection is, thus, a key aspect of this treatment. This paper investigates a promising detection technique, based on the so-called ionoacoustic effect. The BP energy deposition causes a small (millikelvin) heating of the surrounding volume that in turn induces a pressure variation. This generates a sound signal that can be detected by an acoustic sensor placed at a certain distance from the BP point. Thus, the sound time-of-flight measure aims, with sound speed, to detect the BP position with very high accuracy (<1 mm). This paper presents the results of a complete cross-domain model that starting from proton beam energy provides the induced pressure variation in water, emulates the propagation of sound waves in the medium, and finally, returns a voltage signal whose time evolution determines BP position with an average deviation from effective position of 1% accuracy.