Source location as a feature for the classification of multi-sensor extracellular action potentials

Extracellular action potentials (EAPs) must be classified before they can yield any useful information on neuronal function and organization. Neuronal source classification therefore represents a critical step in the analysis of electrophysiological data. This study demonstrates the efficacy of a mu...

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Bibliographic Details
Published in2013 6th International IEEE/EMBS Conference on Neural Engineering (NER) pp. 235 - 238
Main Authors Szymanska, Agnieszka A., Hajirasooliha, Ashkan, Nenadic, Zoran
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2013
Subjects
Algorithm design and analysis
Arrays
Extracellular
Multiple signal classification
Neurons
Noise
Position measurement
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ISSN1948-3546
DOI10.1109/NER.2013.6695915

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Summary:Extracellular action potentials (EAPs) must be classified before they can yield any useful information on neuronal function and organization. Neuronal source classification therefore represents a critical step in the analysis of electrophysiological data. This study demonstrates the efficacy of a multi-sensor EAP classification scheme using source location as a classification feature. Localization was performed using the multiple signal classification (MUSIC) algorithm. Six distinct source neurons were classified from 20 seconds of extracellular, four-sensor (tetrode) recordings. On average, 89.5% of the waveforms making up each class matched the shape of the average class waveform. These results indicate that this classification scheme can successfully identify individual neurons from multi-sensor EAP recordings.
ISSN:1948-3546
DOI:10.1109/NER.2013.6695915