An Adaptive Embedded Platform to Enable Real-Time Brain Motor Decoding

Brain signal decoding combined with spinal cord stimulation have been used in early clinical trials, to restore mobility to paraplegic and tetraplegic patients. Making such systems available for home use requires them to be portable, energy-efficient, and capable of real-time operation. In addition, the decoding model needs to be adaptive to account for the evolution of the brain-machine connection. We present a case study of a Brain-Computer Interface (BCI) that was ported to an embedded platform, resulting in an over $10 \times$ reduction in power consumption, compared to the previous implementation, while maintaining the real-time constraint. The adaptive incremental model update, which preserves a high decoding accuracy over time, was optimized to meet real-time constraints, enabling updates in less than 15 seconds. We explain several algorithmic and implementation techniques that were deployed to reduce the execution time of our C++ embedded implementation by $2 \times$. Furthermore, two different embedded systems were studied and we identified one that was better suited for inference-only use, while the other was better when the model needed to be updated. This case study brings us a step closer to deploying adaptive BCI systems for home use.