Caveats with Stochastic Gradient and Maximum Likelihood Based ICA for EEG
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- 关键词：Independent component analysis (ICA) ; Maximum likelihood ; Stochastic gradient method ; Infomax ; Electroencephalography (EEG) ; Neuroscience
- 刊名：Lecture Notes in Computer Science
- 出版年：2017
- 出版时间：2017
- 年：2017
- 卷：10169
- 期：1
- 页码：279-289
- 丛书名：Latent Variable Analysis and Signal Separation
- ISBN：978-3-319-53547-0
- 卷排序：10169
文摘
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 electroencephalography (EEG) data, we report some caveats which can impact the results and interpretation 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 pushing the convergence of Infomax using non stochastic solvers can reduce the number of highly dipolar components and provide a mathematical explanation of this fact. Results are presented on public EEG data.