Plug-and-play control of a brain-computer interface through neural map stabilization

Nat Biotechnol. 2021 Mar;39(3):326-335. doi: 10.1038/s41587-020-0662-5. Epub 2020 Sep 7.


Brain-computer interfaces (BCIs) enable control of assistive devices in individuals with severe motor impairments. A limitation of BCIs that has hindered real-world adoption is poor long-term reliability and lengthy daily recalibration times. To develop methods that allow stable performance without recalibration, we used a 128-channel chronic electrocorticography (ECoG) implant in a paralyzed individual, which allowed stable monitoring of signals. We show that long-term closed-loop decoder adaptation, in which decoder weights are carried across sessions over multiple days, results in consolidation of a neural map and 'plug-and-play' control. In contrast, daily reinitialization led to degradation of performance with variable relearning. Consolidation also allowed the addition of control features over days, that is, long-term stacking of dimensions. Our results offer an approach for reliable, stable BCI control by leveraging the stability of ECoG interfaces and neural plasticity.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adaptation, Physiological
  • Brain Mapping / methods
  • Brain-Computer Interfaces*
  • Electroencephalography / methods
  • Humans
  • Motor Cortex / physiology
  • Motor Cortex / physiopathology
  • Neuronal Plasticity
  • Paralysis / physiopathology
  • Psychomotor Performance
  • Self-Help Devices