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

@article{Kim2000NeuralSS,
  title={Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier},
  author={Kyung Hwan Kim and Sung June Kim},
  journal={IEEE Transactions on Biomedical Engineering},
  year={2000},
  volume={47},
  pages={1406-1411}
}
Reports 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… 

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