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Making brain-machine interfaces robust to future neural variability.
- Source :
-
Nature communications [Nat Commun] 2016 Dec 13; Vol. 7, pp. 13749. Date of Electronic Publication: 2016 Dec 13. - Publication Year :
- 2016
-
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.
- Subjects :
- Animals
Brain Mapping
Macaca mulatta
Male
Brain-Computer Interfaces
Nerve Net
Subjects
Details
- Language :
- English
- ISSN :
- 2041-1723
- Volume :
- 7
- Database :
- MEDLINE
- Journal :
- Nature communications
- Publication Type :
- Academic Journal
- Accession number :
- 27958268
- Full Text :
- https://doi.org/10.1038/ncomms13749