This article describes a study on neural noise and neural signal feature extraction, targeting real-time spike sorting with miniaturized microchip implementation. Neuronal signature, noise shaping, and adaptive bandpass filtering are reported as the techniques to enhance the signal-to-noise ratio (SNR). A subset of informative samples of the waveforms is extracted as features for classification. Quantitative and comparative experiments with both synthesized and animal data are included to evaluate different feature extraction approaches. In addition, a preliminary hardware implementation has been realized using an integrated circuit.
Bibliographical noteFunding Information:
The authors acknowledge the founding provided by the USA National Science Foundation through BMES-ERC and UC Lab Fee Program. The authors acknowledge the start-up grant provided by National University of Singapore. The authors are grateful to Dr. Victor Pikov, Eric Basham, Plexon, and BMES-ERC Cortical Testbed for providing in vivo neural data and suggestions. The authors acknowledge the in vivo database contributed by Gyorgy Buzsáki lab (http:// crcns.org/data-sets/hc/) and synthesized database contributed by Quian Quiroga (http://www.vis.caltech. edu/~rodri/Wave_clus/Wave_clus_home.htm).
- Action potential
- Spike feature extraction
- Spike sorting