Increasing Robustness of Brain–Computer Interfaces Through Automatic Detection and Removal of Corrupted Input Signals

For brain–computer interfaces (BCIs) to be viable for long-term daily usage, they must be able to quickly identify and adapt to signal disruptions. Furthermore, the detection and mitigation steps need to occur automatically and without the need for user intervention while also being computationally...

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Main Authors: Jordan L. Vasko, Laura Aume, Sanjay Tamrakar, Samuel C. IV Colachis, Collin F. Dunlap, Adam Rich, Eric C. Meyers, David Gabrieli, David A. Friedenberg
Formato: Artigo
Idioma:Inglês
Publicado em: Frontiers Media S.A. 2022-04-01
Colecção:Frontiers in Neuroscience
Assuntos:
brain–machine (computer interface)
neuroprosthetic
deep learning – artificial neural network
intracortical array
statistical process control
Acesso em linha:https://www.frontiersin.org/articles/10.3389/fnins.2022.858377/full
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