Dictionary-based classification models. Applications for multichannel neural activity analysis

Vincent Vigneron*, Hsin Chen, Yen Tai Chen, Hsin Yi Lai, You Yin Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

We describe in this paper advanced protocols for the discrimination and classification of neuronal spike waveforms within multichannel electrophysiological recordings. The programs are capable of detecting and classifying the spikes from multiple, simultaneously active neurons, even in situations where there is a high degree of spike waveform superposition on the recording channels. Sparse Decomposition (SD) approach was used to define the linearly independent signals underlying sensory information in cortical spike firing patterns. We have investigated motor cortex responses recorded during movement in freely moving rats to provide evidence for the relationship between these patterns and special behavioral task. Ensembles of neurons were simultaneously recorded in this during long periods of spontaneous behaviour. Waveforms provided from the neural activity were then processed and classified. Typically, most information correlated across neurons in the ensemble were concentrated in a small number of signals. This showed that these encoding vectors functioned as a feature detector capable of selectively predicting significant sensory or behavioural events. Thus it encoded global magnitude of ensemble activity, caused either by combined sensory inputs or intrinsic network activity. SD on an overcomplete dictionary has recently attracted a lot of attention in the literature, because of its potential application in many different areas including Compressive Sensing (CS). SD approach is compared to the generative approach derived from the likelihood-based framework, in which each class is modeled by a known or unknown density function. The classification of electroencephalographic (EEG) waveforms present 2 main statistical issues: high dimensional data and signal representation.

Original languageEnglish
Title of host publicationEngineering Applications of Neural Networks - 11th International Conference, EANN 2009, Proceedings
PublisherSpringer Verlag
Pages378-388
Number of pages11
ISBN (Print)3642039685, 9783642039683
DOIs
StatePublished - 2009

Publication series

NameCommunications in Computer and Information Science
Volume43 CCIS
ISSN (Print)1865-0929

Keywords

  • Atomic Decomposition
  • Classification
  • Overcomplete Signal Representation
  • Sparse Decomposition

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