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

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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Bibliographic Details
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
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-04-01
Series:Frontiers in Neuroscience
Subjects:
brain–machine (computer interface)
neuroprosthetic
deep learning – artificial neural network
intracortical array
statistical process control
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2022.858377/full

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https://www.frontiersin.org/articles/10.3389/fnins.2022.858377/full

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