Abstract:
In neuroscience, the extracellular actions potentials of neurons are the most important signals, which are called spikes. However, a single extracellular electrode can ca...Show MoreMetadata
Abstract:
In 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.
Date of Conference: 09-13 June 2014
Date Added to IEEE Xplore: 08 September 2014
Electronic ISBN:978-1-4799-5227-4