  We propose to fuse two currently separate research lines on novel therapies
for stroke rehabilitation: brain-computer interface (BCI) training and
transcranial electrical stimulation (TES). Specifically, we show that BCI
technology can be used to learn personalized decoding models that relate the
global configuration of brain rhythms in individual subjects (as measured by
EEG) to their motor performance during 3D reaching movements. We demonstrate
that our models capture substantial across-subject heterogeneity, and argue
that this heterogeneity is a likely cause of limited effect sizes observed in
TES for enhancing motor performance. We conclude by discussing how our
personalized models can be used to derive optimal TES parameters, e.g.,
stimulation site and frequency, for individual patients.
