Adaptive Neural Decoder for Prosthetic Hand Control - Montgomery, Andrew E.; Allen, John M.; Elbasiouny, Sherif M. (Frontiers in Neuroscience, 2021)
The overarching goal was to resolve a major barrier to real-life prosthesis usability – the rapid degradation of prosthesis control systems, which require frequent recalibrations. Specifically, we sought to develop and test a motor decoder that provides 1) highly accurate, real-time movement response, and 2) unprecedented adaptability to dynamic changes in the amputee’s biological state, thereby supporting long-term integrity of control performance with few recalibrations. To achieve that, an adaptive motor decoder was designed to auto-switch between algorithms in real-time. The decoder detects the initial aggregate motoneuron spiking activity from the motor pool, then engages the optimal parameter settings for decoding the motoneuron spiking activity in that particular state. “Clear-box” testing of decoder performance under varied physiological conditions and post-amputation complications was conducted by comparing the movement output of a simulated prosthetic hand as driven by the decoded signal vs. as driven by the actual signal. Pearson’s correlation coefficient and Normalized Root Mean Square Error were used to quantify the accuracy of the decoder’s output. Our results show that the decoder algorithm extracted the features of the intended movement and drove the simulated prosthetic hand accurately with real-time performance (<10ms) (Pearson’s correlation coefficient >0.98->0.99 and Normalized Root Mean Square Error <13%-5%). Further, the decoder robustly decoded the spiking activity of multi-speed inputs, inputs generated from reversed motoneuron recruitment, and inputs reflecting substantial biological heterogeneity of motoneuron properties, also in real-time. As the amputee’s neuromodulatory state changes throughout the day and the electrical properties and ratio of slower vs. faster motoneurons shift over time post-amputation, the motor decoder presented here adapts to such changes in real-time and is thus expected to greatly enhance and extend the usability of prostheses.