Improved sparse bump modeling for electrophysiological data

Authors:
François-Benoit Vialatte;Justin Dauwels;Jordi Solé-Casals;Monique Maurice;Andrzej Cichocki
Affiliations:
Riken BSI, Wako-Shi, Japan;Riken BSI, Wako-Shi, Japan and Massachusetts Institute of Technology, Cambridge, MA;University of Vic, Vic, Spain;Riken BSI, Wako-Shi, Japan;Riken BSI, Wako-Shi, Japan
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

Citing 7
Cited 0

Numerical recipes in C: the art of scientific computing

Numerical recipes in C: the art of scientific computing
Evolving Connectionist Systems: The Knowledge Engineering Approach

Evolving Connectionist Systems: The Knowledge Engineering Approach
A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics

Neural Networks
The IFAST model, a novel parallel nonlinear EEG analysis technique, distinguishes mild cognitive impairment and Alzheimer's disease patients with high degree of accuracy

Artificial Intelligence in Medicine
Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators

Computer Methods and Programs in Biomedicine
Sparse bump sonification: a new tool for multichannel EEG diagnosis of mental disorders; application to the detection of the early stage of alzheimer's disease

ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Early detection of alzheimer’s disease by blind source separation, time frequency representation, and bump modeling of EEG signals

ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Bump modeling is a method used to extract oscillatory bursts in electrophysiological signals, who are most likely to be representative of local synchronies. In this paper we present an improved sparse bump modeling method. The improvements are done in the adaptation method by optimizing the parameters according to the order of their derivatives; and in the window matching method by changing the selection of the initial function. Experimental results, comparing previous method vs the improved version, show that the obtained model fits better the signal, hence the result will be much more precise and useful.