Overview of SELSER
SELSER (Sparse EEG Latent SpacE Regression) is an ML algorithm developed for treatment prediction using pre-treatment EEG. The algorithm first optimizes a sparse set of spatial filters that map the EEG signals to a latent space, and then relates the band powers of the latent signals to the treatment outcome via a linear regression model. The unknown parameters in the spatial filters and linear regression model are optimized in conjunction under a convex optimization framework, where the accelerated proximal gradient method is employed to efficiently solve the optimization problem.
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