Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining data across multiple subjects is crucial.

Here the concept of Granger causality is employed to propose a new criterion for unsupervised learning that is appropriate in the case of temporally-dependent source signals. The basic idea is to identify two projections of a multivariate time series such that the Granger causality among the resulting pair of components is maximized.

Subjects watched on average 2.6 Hollywood movies, for

By predicting the user's intended

Neural Information Processing Systems http://nips.cc/

Toaddressthesechallenges,weproposeMiSO(MicroStimulationOptimization), a closed-loop stimulation framework to drive neural population activity toward specified states by optimizing over a large stimulation parameter space.