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.
Bibliographical noteFunding Information:
Manuscript received May 3, 1999; revised May 30, 2000. This work was supported by the Ministry of Health and Welfare, Korea, under Grant HMP-98-E-1-0006. Asterisk indicates corresponding author. K. H. Kim is with the School of Electrical Engineering, Seoul National University, Kwanak-gu, Seoul 151–742, Korea (e-mail: email@example.com). *S. J. Kim is with the School of Electrical Engineering, Seoul National University, San 56–1, Shillim-dong, Kwanak-gu, Seoul 151–742, Korea (e-mail: firstname.lastname@example.org). Publisher Item Identifier S 0018-9294(00)08531-1.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering