Investigating the Feasibility and Safety of Osseointegration With Neural Interfaces for Advanced Prosthetic Control

Abstract

Osseointegrated neural interfaces (ONI), particularly in conjunction with peripheral nerve interfaces (PNIs), have emerged as a promising advancement for intuitive neuroprosthetics. PNIs can decode neural signals and allow precise prosthetic movement control and bidirectional communication for haptic feedback, while osseointegration can address limitations of traditional socket-based prosthetics, such as poor stability, limited dexterity, and lack of sensory feedback. This review explores advancements in ONIs, including screw-fit and press-fit systems and their integration with PNIs for bidirectional communication. ONIs integrated with PNIs (OIPNIs) have shown improvements in signal fidelity, motor control, and sensory feedback compared to popular surface electromyography (sEMG) systems. Additionally, emerging technologies such as hybrid electrode designs (e.g., cuff and sieve electrode (CASE)) and regenerative peripheral nerve interfaces (RPNIs) show potential for enhancing selectivity and reducing complications such as micromotion and scarring. Despite these advances, challenges remain, including infection risk, electrode degradation, and variability in long-term signal stability. Osseointegration combined with advanced neural interfaces represents a transformative approach to prosthetic control, offering more natural and intuitive movement with sensory feedback. Further research is needed to address long-term biocompatibility, reduce surgical invasiveness, and explore emerging technologies such as machine learning for personalized ONI designs. The findings of this review underscore the potential of ONIs to enhance embodiment and quality of life for amputees and highlight current pitfalls and possible areas of improvement and future research.

Keywords: advanced prosthetics; brain computer interface; neural interfaces; neurosurgery; osseointegration; peripheral nerve interfaces; prosthetic control; regenerative peripheral nerve interfaces.

Figure 1. A summary of the osseointegrated neural interface mechanism.

An action potential is sent from the brain to the peripheral nerve, which is read by the electrodes surrounding the nerve. These electrodes capture the electrical signals and transmit them to a decoder, typically an algorithm or computational model trained to interpret neural signals. The decoder translates the neural activity into specific motor commands, which are then used to actuate the intended movement in the prosthetic limb. The authors created the figure using BioRender (BioRender, Toronto, Canada).

Figure 2. Press-fit systems, screw-fit systems, and osseointegrated prosthetic limb (OPL) systems.

Screw-fit systems thin out the subcutaneous fat tissue layer covering the periosteum to create a tight skin-bone interface, which prevents chronic infection [10]. Screw-fit implants also allow electrodes to exit the abutment. On the far right is an OPL implant within the femur with an intramedullary stem and plasma-sprayed porous titanium coating. There are longitudinal spines on the intramedullary stem for rotational stability [13]. The authors created the figure using BioRender (BioRender, Toronto, Canada). OPL: osseointegrated prosthetic limb

Figure 3. An overview of the regenerative peripheral nerve interface (RPNI) mechanism.

First, a motor signal is sent from the brain to the muscle graft containing the implanted nerve. Then, the signal is amplified by RPNI and recorded by implanted electrodes. Signals are processed and decoded by algorithms to identify intended movements. Finally, the corresponding movement is actuated in the limb. The authors created the figure using BioRender (BioRender, Toronto, Canada).