Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models.
This paper proposes a divide-and-conquer strategy for designing brain machine interfaces. A nonlinear combination of competitively trained local linear models (experts) is used to identify the mapping from neuronal activity in cortical areas associated with arm movement to the hand position of a primate. The proposed architecture and the training algorithm are described in detail and numerical performance comparisons with alternative linear and nonlinear modeling approaches, including time-delay neural networks and recursive multilayer perceptrons, are presented. This new strategy allows training the local linear models using normalized LMS and using a relatively smaller nonlinear network to efficiently combine the predictions of the linear experts. This leads to savings in computational requirements, while the performance is still similar to a large fully nonlinear network.
Duke Scholars
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Related Subject Headings
- Nonlinear Dynamics
- Brain
- Artificial Intelligence & Image Processing
- Artificial Intelligence
- 4905 Statistics
- 4611 Machine learning
- 4602 Artificial intelligence
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Nonlinear Dynamics
- Brain
- Artificial Intelligence & Image Processing
- Artificial Intelligence
- 4905 Statistics
- 4611 Machine learning
- 4602 Artificial intelligence