Making brain-machine interfaces robust to future neural variability

19 Oct 2016David SussilloSergey D. StaviskyJonathan C. KaoStephen I. RyuKrishna V. Shenoy

A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of previously collected data as well as synthetic training data perturbations... (read more)

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