Name
Title | ||
|---|---|---|
Sparse multivariate autoregressive models with exogenous inputs for modeling intracerebral responses to direct electrical stimulation of the human brain |
Abstract | ||
|---|---|---|
The self-connected group lasso is used to estimate sparse multivariable autoregressive with exogenous (MVARX) input models of the cortical interactions excited by direct current stimulation of the cortex. The group lasso criterion introduces a direct network connection between two sites only if the presence of the connection significantly reduces the mean-squared error of the model. This method is applied to intracranial recordings of the human brain to direct electrical stimulation. Excellent agreement between measured and model-predicted average responses across all data sets is obtained. One-step prediction of the recordings is also used to demonstrate that the model describes the dynamics in individual responses. We study the similarity of network models for a given set of channels when the electrical stimulation is applied at different locations in both wakefulness and nonrapid eye movement (NREM) sleep to identify common network characteristics. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ACSSC.2013.6810397 | ACSSC |
Keywords | Field | DocType |
bioelectric potentials,sparse multivariable autoregressive with exogenous input models,nrem,mvarx,direct electrical stimulation,electroencephalography,nonrapid eye movement sleep,magneto-electroencephalography,mean-squared error,intracerebral response modelling,medical signal processing,one-step prediction,autoregressive processes,mvarx input models,self-connected group lasso criterion,m/eeg,brain,magnetoencephalography,network inference,sparsity,group lasso,cortical interactions,model-predicted average responses,granger causality,intracranial recordings,human brain,sleep,predictive models,silicon,data models | Data modeling,Data set,Computer science,Eye movement,Artificial intelligence,Stimulation,Autoregressive model,Mathematical optimization,Pattern recognition,Non-rapid eye movement sleep,Wakefulness,Machine learning,Network model | Conference |
ISSN | ISBN | Citations |
1058-6393 | 978-1-4799-2388-5 | 0 |
PageRank | References | Authors |
0.34 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jui-Yang Chang | 1 | 0 | 0.34 |
| Andrea Pigorini | 2 | 12 | 2.88 |
| Francesca Seregni | 3 | 0 | 0.34 |
| Marcello Massimini | 4 | 60 | 14.56 |
| Lino Nobili | 5 | 20 | 7.45 |
| Barry Van Veen | 6 | 30 | 2.01 |