Optimal detection, classification, and superposition resolution in neural waveform recordings

I N Bankman; K O Johnson; W Schneider

doi:10.1109/10.238472

Optimal detection, classification, and superposition resolution in neural waveform recordings

IEEE Trans Biomed Eng. 1993 Aug;40(8):836-41. doi: 10.1109/10.238472.

Authors

I N Bankman¹, K O Johnson, W Schneider

Affiliation

¹ Applied Physics Laboratory, Johns Hopkins University, Laurel, MD 20723.

PMID: 8258452
DOI: 10.1109/10.238472

Abstract

The effects of noise autocorrelation on neural waveform recognition (detection, classification, and superposition resolution) are investigated in this study using microelectrode recordings from the cortex of a monkey. Optimal waveform recognition is accomplished by passing the data through a whitening filter before matched filtering for detection or template matching for classification and superposition resolution. Template matching without whitening requires about 40% higher signal-to-noise ratio than template matching with whitening for comparable classification and superposition resolution. The comparable difference for detection is 15%.

Publication types

Comparative Study
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, U.S. Gov't, P.H.S.

MeSH terms

Action Potentials
Animals
Artifacts
Bayes Theorem
Electrophysiology / classification
Electrophysiology / methods
Electrophysiology / statistics & numerical data
Haplorhini
Neurons / physiology*
Somatosensory Cortex / physiology

Grants and funding

NS 07226/NS/NINDS NIH HHS/United States