Psychology

Neural activity

Artificial intelligence

Neuroscience

Interface (matter)

Bubble

Human–computer interaction

Operating system

Computer science

Control (management)

Maximum bubble pressure method

Plug and play

Brain–computer interface

Artificial neural network

Electroencephalography

Nature Biotechnology

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. A paralyzed individual controls a neuroprosthetic without daily recalibration.

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