Document Detail

Automatic spike sorting for neural decoding.
MedLine Citation:
PMID:  17271178     Owner:  NLM     Status:  PubMed-not-MEDLINE    
While various automated spike sorting techniques have been developed, their impact on neural decoding has not been investigated. In this paper we extend previous Gaussian mixture models and expectation maximization (EM) techniques for automatic spike sorting. We suggest that good initialization of EM is critical and can be achieved via spectral clustering. To account for noise we extend the mixture model to include a uniform outlier process. Automatically determining the number of neurons recorded per electrode is a challenging problem which we solve using a greedy optimization algorithm that selects models with different numbers of neurons according to their decoding accuracy. We focus on data recorded from motor cortex and evaluate performance with respect to the decoding of hand kinematics from firing rates. We found that spike trains obtained by our automated technique result in more accurate neural decoding than those obtained by human experts.
F Wood; M Fellows; J Donoghue; M Black
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference     Volume:  6     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2004  
Date Detail:
Created Date:  2007-02-02     Completed Date:  2007-09-20     Revised Date:  2014-08-21    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  4009-12     Citation Subset:  -    
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