Decomposition of EEG signals for multichannel neural activity analysis in animal experiments

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

2 Scopus citations


We describe in this paper some advanced protocols for the discrimination and classification of neuronal spike waveforms within multichannel electrophysiological recordings. Sparse decomposition was used to serarate the linearly independent signals underlying sensory information in cortical spike firing pat- terns. We introduce some modifications in the the IDE algorithm to take into account prior knowledge on the spike waveforms. We have investigated motor cortex responses recorded during movement in freely moving rats to provide ev- idence for the relationship between these patterns and special behavioral task.

Original languageEnglish
Title of host publicationLatent Variable Analysis and Signal Separation - 9th International Conference, LVA/ICA 2010, Proceedings
PublisherSpringer Verlag
Number of pages8
ISBN (Print)364215994X, 9783642159947
StatePublished - 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6365 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Atomic Decomposition
  • IDE akgorithm
  • Sparse decomposition
  • classification
  • semi-supervised learning


Dive into the research topics of 'Decomposition of EEG signals for multichannel neural activity analysis in animal experiments'. Together they form a unique fingerprint.

Cite this