On the variability of manual spike sorting.
The 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.
Wood
Frank
F
Department of Computer Science, Brown University, Providence, RI 02912, USA. fwood@cs.brown.edu
Black
Michael J
MJ
Vargas-Irwin
Carlos
C
Fellows
Matthew
M
Donoghue
John P
JP
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005188:False Negative Reactions; D005189:False Positive Reactions; D000882:Haplorhini; D009044:Motor Cortex / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013647:Task Performance and Analysis; D014584:User-Computer Interface