Recognition of multiunit neural signals

An essential step in studying nerve cell interaction during information processing is the extracellular microelectrode recording of the electrical activity of groups of adjacent cells. The recording usually contains the superposition of the spike trains produced by a number of neurons in the vicinit...

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Bibliographic Details
Published inIEEE transactions on biomedical engineering Vol. 39; no. 7; pp. 723 - 729
Main Author Atiya, A.F.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.07.1992
Institute of Electrical and Electronics Engineers
Subjects
Action Potentials
Algorithms
Bias
Biological and medical sciences
Classification
Electrodes
Evaluation Studies as Topic
Extracellular
Humans
Information processing
Investigative techniques, diagnostic techniques (general aspects)
Medical sciences
Microelectrodes
Motor Neurons - physiology
Nervous system
Neurons
Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques
Probability
Probability Learning
Shape
Signal generators
Signal Processing, Computer-Assisted
Testing
Human
Spike
Electrical activity
Interaction
Experimental study
Properties
Essential
Statistics
Neuron
Classification
Information processing
Microelectrode
Technique
Recognition
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Summary:An essential step in studying nerve cell interaction during information processing is the extracellular microelectrode recording of the electrical activity of groups of adjacent cells. The recording usually contains the superposition of the spike trains produced by a number of neurons in the vicinity of the electrode. It is therefore necessary to correctly classify the signals generated by these different neurons. This problem is considered, and a new classification scheme is developed which does not require human supervision. A learning stage is first applied on the beginning portion of the recording to estimate the typical spike shapes of the different neurons. As for the classification stage, a method is developed which specifically considers the case when spikes overlap temporally. The method minimizes the probability of error, taking into account the statistical properties of the discharges of the neurons. The method is tested on a real recording as well as on synthetic data.< >
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ISSN:0018-9294
1558-2531
DOI:10.1109/10.142647