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Real-time neural signals decoding onto off-the-shelf DSP processors for neuroprosthetic applications.

Pani, D and Barabino, G and Citi, L and Meloni, P and Raspopovich, S and Micera, S and Raffo, L (2016) 'Real-time neural signals decoding onto off-the-shelf DSP processors for neuroprosthetic applications.' IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2 (9). pp. 993-1002. ISSN 1534-4320

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Abstract

The control of upper limb neuroprostheses through the peripheral nervous system (PNS) can allow restoring motor functions in amputees. At present, the important aspect of the real-time implementation of neural decoding algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited hardware resources have on the efficiency/effectiveness of any given algorithm. Present study is addressing the optimization of a template matching based algorithm for PNS signals decoding that is a milestone for its real-time, full implementation onto a floating-point Digital Signal Processor (DSP). The proposed optimized real-time algorithm achieves up to 96% of correct classification on real PNS signals acquired through LIFE electrodes on animals, and can correctly sort spikes of a synthetic cortical dataset with sufficiently uncorrelated spike morphologies (93% average correct classification) comparably to the results obtained with top spike sorter (94% on average on the same dataset). The power consumption enables more than 24 hours processing at the maximum load, and latency model has been derived to enable a fair performance assessment. The final embodiment demonstrates the real-time performance onto a low-power off-the-shelf DSP, opening to experiments exploiting the efferent signals to control a motor neuroprosthesis.

Item Type: Article
Uncontrolled Keywords: spike sorting; Digital signal processing chips; embedded software; neural prosthesis; real-time systems
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
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: 18 Oct 2016 11:50
Last Modified: 15 Jan 2022 00:20
URI: http://repository.essex.ac.uk/id/eprint/17793

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