Adaptive brain-computer interface decoding method based on multi-model dynamic integration
US12106204B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 27, 2021 |
| Grant date | Oct 1, 2024 |
| Priority date | — |
| Expiry date | Oct 27, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present invention discloses an adaptive brain-computer interface decoding method based on multi-model dynamic ensemble, where a traditional state-space model is improved, and a set of measurement functions instead of one fixed measurement function are used to dynamically characterize a relationship between observation variables and state variables; and, by using a pool of linear and nonlinear decoders, and in a decoding process of a brain-computer interface system, decoders are automatically switched according to the data, so as to realize adaptive brain signal decoding. Through the above multi-model ensemble strategy, linear and nonlinear decoder capabilities can be integrated, the accuracy and stability of the brain-computer interface system can be improved, and decoding unstability caused by the non-stationary neural signal of the brain-computer interface system can be solved to a certain extent.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.