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Hidden Markov model neurons classification based on Mel-frequency cepstral coefficients
conference contribution
posted on 2014-01-01, 00:00 authored by Sherif Haggag, Shady MohamedShady Mohamed, Hussein Haggag, Saeid NahavandiIn neuroscience, the extracellular actions potentials of neurons are the most important signals, which are called spikes. However, a single extracellular electrode can capture spikes from more than one neuron. Spike sorting is an important task to diagnose various neural activities. The more we can understand neurons the more we can cure more neural diseases. The process of sorting these spikes is typically made in some steps which are detection, feature extraction and clustering. In this paper we propose to use the Mel-frequency cepstral coefficients (MFCC) to extract spike features associated with Hidden Markov model (HMM) in the clustering step. Our results show that using MFCC features can differentiate between spikes more clearly than the other feature extraction methods, and also using HMM as a clustering algorithm also yields a better sorting accuracy.
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Pagination
166 - 170Publisher DOI
ISBN-13
9781479952274Publication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, Institute of Electrical and Electronics EngineersTitle of proceedings
Proceedings of the 9th International Conference on System of Systems Engineering: The Socio-Technical Perspective, SoSE 2014Usage metrics
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