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- Frank Wood, Michael J Black, Carlos Vargas-Irwin, Matthew Fellows, and John P Donoghue.
- Department of Computer Science, Brown University, Providence, RI 02912, USA. fwood@cs.brown.edu
- IEEE Trans Biomed Eng. 2004 Jun 1; 51 (6): 912-8.
AbstractThe analysis of action potentials, or "spikes," is central to systems neuroscience research. Spikes are typically identified from raw waveforms manually for off-line analysis or automatically by human-configured algorithms for on-line applications. The variability of manual spike "sorting" is studied and its implications for neural prostheses discussed. Waveforms were recorded using a micro-electrode array and were used to construct a statistically similar synthetic dataset. Results showed wide variability in the number of neurons and spikes detected in real data. Additionally, average error rates of 23% false positive and 30% false negative were found for synthetic data.
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