On the variability of manual spike sorting

Author

Wood, Frank ; Black, Michael J. ; Vargas-Irwin, Carlos ; Fellows, Matthew ; Donoghue, John P.

Author_Institution

Dept. of Comput. Sci., Brown Univ., Providence, RI, USA

Volume

51

Issue

6

fYear

2004

fDate

6/1/2004 12:00:00 AM

Firstpage

912

Lastpage

918

Abstract

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.

Keywords

bioelectric potentials; microelectrodes; neurophysiology; prosthetics; waveform analysis; action potentials; manual spike sorting; microelectrode array; neural prostheses; neurons; systems neuroscience research; waveforms; Computer science; Decoding; Electrodes; Electrophysiology; Neural prosthesis; Neurons; Neuroscience; Prosthetics; Signal to noise ratio; Sorting; Action Potentials; Algorithms; Animals; Diagnosis, Computer-Assisted; Electroencephalography; False Negative Reactions; False Positive Reactions; Haplorhini; Motor Cortex; Neurons; Observer Variation; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Task Performance and Analysis; User-Computer Interface;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/TBME.2004.826677

Filename

1300782