Making brain–machine interfaces robust to future neural variability | Nature Communications

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Download PDF Subjects Biotechnology Brain–machine interface A Corrigendum to this article was published on 20 January 2017 This article has been updated Abstract 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 synthet

Making brain–machine interfaces robust to future neural variability | Nature Communications

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