Comparison of Sneo-Based Neural Spike Detection Algorithms for Implantable Multi-Transistor Array Biosensors
| Autor: | Antonio G. M. Strollo, Gerardo Saggese, Stefano Vassanelli, Andrea Baschirotto, Elia Arturo Vallicelli, Mattia Tambaro, Marcello De Matteis |
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| Přispěvatelé: | Saggese, G, Tambaro, M, Vallicelli, E, Strollo, A, Vassanelli, S, Baschirotto, A, De Matteis, M, Saggese, G., Tambaro, M., Vallicelli, E. A., Strollo, A. G. M., Vassanelli, S., Baschirotto, A., De Matteis, M. |
| Rok vydání: | 2021 |
| Předmět: |
Noise power
Computer Networks and Communications Computer science 0206 medical engineering lcsh:TK7800-8360 Spike detection 02 engineering and technology Noise estimator 03 medical and health sciences 0302 clinical medicine Low power Sensitivity (control systems) Electrical and Electronic Engineering Digital signal processing noise estimators Quantitative Biology::Neurons and Cognition business.industry lcsh:Electronics Transistor array 020601 biomedical engineering CMOS Hardware and Architecture Control and Systems Engineering Signal Processing Spike (software development) Brain-silicon interface business Algorithm 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | Electronics Volume 10 Issue 4 Electronics, Vol 10, Iss 410, p 410 (2021) |
| ISSN: | 2079-9292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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