Decomposition of EEG Signals for Multichannel Neural Activity Analysis in Animal Experiments

doi:10.1007/978-3-642-15995-4_59

標題:	Decomposition of EEG Signals for Multichannel Neural Activity Analysis in Animal Experiments
作者:	Vigneron, Vincent Chen, Hsin Chen, Yen-Tai Lai, Hsin-Yi Chen, You-Yin 電機工程學系 Department of Electrical and Computer Engineering
關鍵字:	Sparse decomposition;classification;semi-supervised learning;Atomic Decomposition;IDE akgorithm
公開日期:	2010
摘要:	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 patterns. 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 evidence for the relationship between these patterns and special behavioral task.
URI:	http://dx.doi.org/10.1007/978-3-642-15995-4_59 http://hdl.handle.net/11536/135588
ISBN:	978-3-642-15994-7
ISSN:	0302-9743
DOI:	10.1007/978-3-642-15995-4_59
期刊:	LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION
Volume:	6365
起始頁:	474
結束頁:	+
Appears in Collections:	Conferences Paper