Intuitive control of supernumerary robotic limbs through a tactile-encoded neural interface

TL;DR

This paper introduces a tactile-encoded brain-computer interface that enables intuitive control of supernumerary robotic limbs, achieving reliable decoding of multiple degrees of freedom without disrupting natural movement, and demonstrating practical movement augmentation.

Contribution

The study presents a novel tactile-evoked P300 BCI paradigm that reliably decodes supernumerary limb intentions and integrates sensory feedback for movement augmentation.

Findings

Achieved real-time decoding of four supernumerary degrees of freedom.

Performance improved significantly after three days of training.

Natural movement remained unaffected during concurrent supernumerary control.

Abstract

Brain-computer interfaces (BCIs) promise to extend human movement capabilities by enabling direct neural control of supernumerary effectors, yet integrating augmented commands with multiple degrees of freedom without disrupting natural movement remains a key challenge. Here, we propose a tactile-encoded BCI that leverages sensory afferents through a novel tactile-evoked P300 paradigm, allowing intuitive and reliable decoding of supernumerary motor intentions even when superimposed with voluntary actions. The interface was evaluated in a multi-day experiment comprising of a single motor recognition task to validate baseline BCI performance and a dual task paradigm to assess the potential influence between the BCI and natural human movement. The brain interface achieved real-time and reliable decoding of four supernumerary degrees of freedom, with significant performance improvements after only three days of training. Importantly, after training, performance did not differ significantly between the single- and dual-BCI task conditions, and natural movement remained unimpaired during concurrent supernumerary control. Lastly, the interface was deployed in a movement augmentation task, demonstrating its ability to command two supernumerary robotic arms for functional assistance during bimanual tasks. These results establish a new neural interface paradigm for movement augmentation through stimulation of sensory afferents, expanding motor degrees of freedom without impairing natural movement.