Connectivity estimation of three parametric methods on simulated electroencephalogram signals.
The global framework of this paper is the connectivity estimation in multichannel electroencephalogram (EEG) recordings, modeled as multidimensional autoregressive (AR) processes. The coherence, directed transfer function and partial directed coherence functions are evaluated on two simulated EEG signals for their later application on real EEG recordings. The results were evaluated computing the relative error and a second proposed performance criterion (eta) based on the entropy of the estimated connectivity matrix.
Vélez-Pérez
Hugo
H
Research Centre for Automatic Control, CRANUMR 7039, National Center for Scientific Research, Nancy University, Vandoeuvre les Nancy, France. hugo.velez-perez@ensem.inpl-nancy.fr
Louis-Dorr
Valérie
V
Ranta
Radu
R
Dufaut
Michel
M
D000704:Analysis of Variance; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography; D001330:Electronic Data Processing; D006801:Humans; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D015233:Models, Statistical; D009434:Neural Pathways / Q000502:physiology*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted