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Making brain-machine interfaces robust to future neural variability
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DOI：10.1038/ncomms13749.png)
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Abstract
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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.
Keywords:
LOCAL-FIELD POTENTIALS
MOTOR CORTEX
PREMOTOR
MOVEMENTS
DISCHARGE
FEEDBACK
PEOPLE
SPACE
TERM
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