Making brain–machine interfaces robust to future neural variability

David Sussillo, Sergey D. Stavisky, Jonathan C. Kao, Stephen I. Ryu and Krishna V. Shenoy ()
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David Sussillo: Stanford University
Sergey D. Stavisky: Neurosciences Graduate Program
Jonathan C. Kao: Stanford University
Stephen I. Ryu: Stanford University
Krishna V. Shenoy: Stanford University

Nature Communications, 2016, vol. 7, issue 1, 1-13

Abstract: 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 synthetic training data perturbations. We developed a new multiplicative recurrent neural network BMI decoder that successfully learned a large variety of neural-to-kinematic mappings and became more robust with larger training data sets. Here we demonstrate that when tested with a non-human primate preclinical BMI model, this decoder is robust under conditions that disabled a state-of-the-art Kalman filter-based decoder. These results validate a new BMI strategy in which accumulated data history are effectively harnessed, and may facilitate reliable BMI use by reducing decoder retraining downtime.

Date: 2016
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DOI: 10.1038/ncomms13749

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