Skip to Main content Skip to Navigation
Journal articles

Spike sorting by stochastic simulation.

Abstract : The decomposition of multiunit signals consists of the restoration of spike trains and action potentials in neural or muscular recordings. Because of the complexity of automatic decomposition, semiautomatic procedures are sometimes chosen. The main difficulty in automatic decomposition is the resolution of temporally overlapped potentials. In a previous study , we proposed a Bayesian model coupled with a maximum a posteriori (MAP) estimator for fully automatic decomposition of multiunit recordings and we showed applications to intramuscular EMG signals. In this study, we propose a more complex signal model that includes the variability in amplitude of each unit potential. Moreover, we propose the Markov Chain Monte Carlo (MCMC) simulation and a Bayesian minimum mean square error (MMSE) estimator by averaging on samples that converge in distribution to the joint posterior law. We prove the convergence property of this approach mathematically and we test the method representatively on intramuscular multiunit recordings. The results showed that its average accuracy in spike identification is greater than 90% for intramuscular signals with up to 8 concurrently active units. In addition to intramuscular signals, the method can be applied for spike sorting of other types of multiunit recordings.
Document type :
Journal articles
Complete list of metadatas

Cited literature [34 references]  Display  Hide  Download

https://www.hal.inserm.fr/inserm-00954647
Contributor : Di Ge <>
Submitted on : Monday, March 3, 2014 - 12:30:54 PM
Last modification on : Tuesday, May 12, 2020 - 3:04:15 PM
Long-term archiving on: : Tuesday, June 3, 2014 - 10:52:09 AM

Files

SpikeSorting.pdf
Files produced by the author(s)

Identifiers

Citation

Di Ge, Eric Le Carpentier, Jérôme Idier, Dario Farina. Spike sorting by stochastic simulation.. IEEE Trans Neural Syst Rehabil Eng, 2011, 19 (3), pp.249-59. ⟨10.1109/TNSRE.2011.2112780⟩. ⟨inserm-00954647⟩

Share

Metrics

Record views

537

Files downloads

773