- Recherche et sélection de publications
|
Caveats with stochastic gradient and maximum likelihood based ICA for EEG
- Jair Montoya #1, Jean-François Cardoso #1, Alexandre Gramfort #1
-
- References
- LVA/ICA, Grenoble, February 2017,
- Abstract
Stochastic gradient (SG) is the most commonly used optimization
technique for maximum likelihood based approaches to independent component
analysis (ICA). It is in particular the default solver in public implementations of
Infomax and variants. Motivated by experimental findings on electroencephalog-
raphy (EEG) data, we report some caveats which can impact the results and inter-
pretation of neuroscience findings. We investigate issues raised by controlling the
step size in gradient updates combined with early stopping conditions, as well as
initialization choices which can artificially generate biologically plausible brain
sources, so called dipolar sources. We provide experimental evidence that push-
ing the convergence of Infomax using non stochastic solvers can reduce the num-
ber of highly dipolar components and provide a mathematical explanation of this
fact. Results are presented on public EEG data.
- Keywords
- Independent component analysis (ICA), maximum likelihood, stochas- tic gradient method, infomax, electroencephalography (EEG), neuroscience
- Category
- Paper in proceedings
- Research Area(s)
- Computer Science/Signal and Image Processing
- Identifier(s)
-
Bibliographic key jmontoyaicalva2017
- Export
-
- Last update
- on february 01, 2017 by Jair Montoya Martinez
|