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

Authors:
Vincent Vigneron;Hsin Chen;Yen-Tai Chen;Hsin-Yi Lai;You-Yin Chen
Affiliations:
IBISC, CNRS, Université d'Evry, France;Dept. of Electrical Engineering, National Tsing Hwa University, Taiwan;Dept. of Electrical Engineering, National Chiao-Tung University, Taiwan;Dept. of Electrical Engineering, National Chiao-Tung University, Taiwan;Dept. of Electrical Engineering, National Chiao-Tung University, Taiwan
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

Citing 5
Cited 0

Topographic Independent Component Analysis

Neural Computation
A fast approach for overcomplete sparse decomposition based on smoothed l0 norm

IEEE Transactions on Signal Processing
Fast sparse representation based on smoothed lo norm

ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm

IEEE Transactions on Signal Processing
Uncertainty principles and ideal atomic decomposition

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.