Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier

Kyung Hwan Kim, Sung June Kim

Research output: Contribution to journalArticle

174 Citations (Scopus)


We report a result on neural spike sorting under conditions where the signal-to-noise ratio is very low. The use of nonlinear energy operator enables the detection of an action potential, even when the SNR is so poor that a typical amplitude thresholding method cannot be applied. The superior detection ability facilitates the collection of a training set under lower SNR than that of the methods which employ simple amplitude thresholding. Thus, the statistical characteristics of the input vectors can be better represented in the neural-network classifier. The trained neural-network classifiers yield the correct classification ratio higher than 90% when the SNR is as low as 1.2 (0.8 dB) when applied to data obtained from extracellular recording from Aplysia abdominal ganglia using a semiconductor microelectrode array.

Original languageEnglish
Pages (from-to)1406-1411
Number of pages6
JournalIEEE Transactions on Biomedical Engineering
Issue number10
Publication statusPublished - 2000 Oct 1


All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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