Making brain–machine interfaces robust to future neural variability
Brain-machine interfaces (BMI) depend on algorithms to decode neural signals, but these decoders cope poorly with signal variability. Here, authors report a BMI decoder which circumvents these problems by using a large and perturbed training dataset to improve performance with variable neural signal...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2016-12-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/ncomms13749 |