Optimal detection, classification, and superposition resolution in neural waveform recordings
- 1 January 1993
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Biomedical Engineering
- Vol. 40 (8) , 836-841
- https://doi.org/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%.Keywords
This publication has 17 references indexed in Scilit:
- Automatic classification and analysis of microneurographic spike data using a PC/ATJournal of Neuroscience Methods, 1990
- Unsupervised waveform classification for multi-neuron recordings: a real-time, software-based system. II. Performance comparison to other sortersJournal of Neuroscience Methods, 1988
- Unsupervised waveform classification for multi-neuron recordings: a real-time, software-based system. I. Algorithms and implementationJournal of Neuroscience Methods, 1988
- Automatic decomposition of selective needle-detected myoelectric signalsIEEE Transactions on Biomedical Engineering, 1988
- A totally automated system for the detection and classification of neural spikesIEEE Transactions on Biomedical Engineering, 1988
- A real-time multiprocessor system for acquisition of multichannel neural dataIEEE Transactions on Biomedical Engineering, 1988
- Intelligent software for spike separation in multiunit recordingsMedical & Biological Engineering & Computing, 1987
- Computer separation of multi-unit neuroelectric data: a reviewJournal of Neuroscience Methods, 1984
- Real-Time Classification of Multiunit Neural Signals Using Reduced Feature SetsIEEE Transactions on Biomedical Engineering, 1981
- On-line multi-unit sorting with resolution of superposition potentialsElectroencephalography and Clinical Neurophysiology, 1973