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Another advancement in biomedical engineering that opens opportunities to revise the concept of motor functioning neural linkage to artificial limbs is neuro-adaptive types of prosthetics; this concept faces important issues of variation, models of signal delay, limited dynamic range, and high power needs, hindering the ability to respond in real time to neural impulses and devices to provide user-specific designs. EMG and EEG are inherently noisy neural signals that are vulnerable to variations due to muscle fatigue, electrode movement, or user motion, requiring a control system that can adapt continuously without compromising speed and efficiency. To address these issues, this paper proposes a novel design of self-adaptive control logic architecture utilising Field Programmable Gate Arrays (FPGAs). The proposed architecture combines a real-time adaptive filtering protocol, multi-channel feature generation, and a simplified neuroplasticity-based refresh algorithm. The FPGA-based system leverages parallel hardware execution to reduce processing latency and maximise power efficiency, achieving neural intent decoding speeds and accurately actuating the prosthetic. Experimental analyses with synthetic and real neural data resources demonstrate that the developed system can achieve up to 89% accuracy in decoding motor commands, reduce response time to a minimum of 10 milliseconds, and significantly decrease power consumption (more than 35%) compared to conventional microcontroller solutions. In addition, the adaptive learning category enhances strong functionality in the presence of signal drift and individual physiological variations, thereby offering personalised and trustworthy access to a prosthetic over its prolonged use. These findings demonstrate that the FPGA-implemented self-adaptive control logic represents a significant advancement in the field of neuroprosthetics, as it offers fast, flexible, and energy-efficient capabilities, thereby enhancing user experiences and clinical performance. This is one of the strategies that will provide preparatory grounds for scalable, low-latency, and groundbreaking prosthetic systems, which possess the capacity for real-time neuro-adaptation and eventually lead to improvements in self-reliance and quality of life through long-term reliance on supportive prosthetic systems based on biomedical assumptions.

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A Self-Adaptive FPGA-Based Control Logic for Neuro-Adaptive Prosthetics in Biomedical Applications

  • 会议论文

  • 发表日期：

    2026年

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  • Aakansha Soy,
  • Ashu Nayak

摘要

Another advancement in biomedical engineering that opens opportunities to revise the concept of motor functioning neural linkage to artificial limbs is neuro-adaptive types of prosthetics; this concept faces important issues of variation, models of signal delay, limited dynamic range, and high power needs, hindering the ability to respond in real time to neural impulses and devices to provide user-specific designs. EMG and EEG are inherently noisy neural signals that are vulnerable to variations due to muscle fatigue, electrode movement, or user motion, requiring a control system that can adapt continuously without compromising speed and efficiency. To address these issues, this paper proposes a novel design of self-adaptive control logic architecture utilising Field Programmable Gate Arrays (FPGAs). The proposed architecture combines a real-time adaptive filtering protocol, multi-channel feature generation, and a simplified neuroplasticity-based refresh algorithm. The FPGA-based system leverages parallel hardware execution to reduce processing latency and maximise power efficiency, achieving neural intent decoding speeds and accurately actuating the prosthetic. Experimental analyses with synthetic and real neural data resources demonstrate that the developed system can achieve up to 89% accuracy in decoding motor commands, reduce response time to a minimum of 10 milliseconds, and significantly decrease power consumption (more than 35%) compared to conventional microcontroller solutions. In addition, the adaptive learning category enhances strong functionality in the presence of signal drift and individual physiological variations, thereby offering personalised and trustworthy access to a prosthetic over its prolonged use. These findings demonstrate that the FPGA-implemented self-adaptive control logic represents a significant advancement in the field of neuroprosthetics, as it offers fast, flexible, and energy-efficient capabilities, thereby enhancing user experiences and clinical performance. This is one of the strategies that will provide preparatory grounds for scalable, low-latency, and groundbreaking prosthetic systems, which possess the capacity for real-time neuro-adaptation and eventually lead to improvements in self-reliance and quality of life through long-term reliance on supportive prosthetic systems based on biomedical assumptions.