Towards hippocampal navigation for brain–computer interfaces

Automatic wheelchairs directly controlled by brain activity could provide autonomy to severely paralyzed individuals. Current approaches mostly rely on non-invasive measures of brain activity and translate individual commands into wheelchair movements. For example, an imagined movement of the right...

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Published inScientific reports Vol. 13; no. 1; p. 14021
Main Authors Saal, Jeremy, Ottenhoff, Maarten Christiaan, Kubben, Pieter L, Colon, Albert J, Goulis, Sophocles, van Dijk, Johannes P, Krusienski, Dean J, Herff, Christian
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group 28.08.2023
Nature Publishing Group UK
Nature Portfolio
Subjects
Brain research
Cognition & reasoning
Cognitive ability
Discriminant analysis
Electroencephalography
Eye movements
Hippocampus
Monkeys & apes
Neural networks
Paralysis
Patients
Prosthetics
Robotics
Success
Wheelchairs
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Summary:Automatic wheelchairs directly controlled by brain activity could provide autonomy to severely paralyzed individuals. Current approaches mostly rely on non-invasive measures of brain activity and translate individual commands into wheelchair movements. For example, an imagined movement of the right hand would steer the wheelchair to the right. No research has investigated decoding higher-order cognitive processes to accomplish wheelchair control. We envision an invasive neural prosthetic that could provide input for wheelchair control by decoding navigational intent from hippocampal signals. Navigation has been extensively investigated in hippocampal recordings, but not for the development of neural prostheses. Here we show that it is possible to train a decoder to classify virtual-movement speeds from hippocampal signals recorded during a virtual-navigation task. These results represent the first step toward exploring the feasibility of an invasive hippocampal BCI for wheelchair control.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-40282-7