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Real-time processing of tfLIFE neural signals on embedded DSP platforms: A case study

Pani, Danilo and Usai, Francesco and Citi, Luca and Raffo, Luigi (2011) Real-time processing of tfLIFE neural signals on embedded DSP platforms: A case study. In: 5th International IEEE/EMBS Conference on Neural Engineering (NER 2011), 2011-04-27 - 2011-05-01.

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Abstract

Spike sorting is a typical neural processing technique aimed at identifying the firing activity of individual neurons. It plays a different role in the processing of the signals coming either from a single electrode or an electrode array. In presence of highly noisy recordings, a preliminary denoising stage is required in order to improve the SNR. Despite the significant number of studies in the field, only a few of them deal with peripheral nervous system (PNS) recordings and often the possibility of a real-time implementation is only hinted without any real implementation study. In this paper, a real-time PNS signal processing and classification technique is presented end evaluated on real elec-troneurographic signals taken from the sciatic nerve of rats. A state-of-the-art algorithm, composed of a wavelet denoising preprocessing stage followed by a correlation-based spike sorting and a support vector machine, has been adapted to work on-line in order to improve the processing efficiency while preserving at the most its effectiveness. The algorithm provides some level of adaptiveness with respect to an off-line implementation. On average, the correct classification reach 92.24% with isolated errors that can be easily filtered out. Cycle-accurate profiling results on an off-the-shelf Digital Signal Processor demonstrate the real-time performance. © 2011 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 04 Mar 2014 15:05
Last Modified: 15 Jan 2022 00:43
URI: http://repository.essex.ac.uk/id/eprint/8818

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