e-journal

Improved Signal Processing Methods for Velocity Selective Neural Recording Using Multi-Electrode Cuffs

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This paper describes an improved system for obtaining velocity spectral information from electroneurogram
recordings using multi-electrode cuffs (MECs). The starting point for this study is some recently published work that considers the limitations of conventional linear signal processing methods
(‘delay-and-add’) with and without additive noise. By contrast to earlier linear methods, the present paper adopts a fundamentally non-linear velocity classification approach based on a type of
artificial neural network (ANN). The new method provides a unified approach to the solution of the two main problems of the earlier delay-and-add technique, i.e., a damaging decline in both velocity selectivity and velocity resolution at high velocities. The new method can operate in real-time, is shown to be robust in the presence of noise and also to be relatively insensitive to the form of the action potential waveforms being classified.
Index Terms—Artificial neural networks, biomedical signal processing, biomedical transducers, microelectronic implants, neural prosthesis.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS

No. Panggil

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Penerbit

New York : IEEE., 2014

Deskripsi Fisik

IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, VOL. 8, NO. 3, JUNE 2014

Bahasa

English

ISBN/ISSN

1932-4545

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 8, NO. 3, JUNE 2014

Subjek

TEKNIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

Yuli/Agus

Versi lain/terkait

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