Neuro-Morphic Computing for Brain-Machine Interfaces

EasyChair Preprint 14044

Abstract

Neuro-morphic computing, which integrates principles from neuroscience, microelectronics, and computer science, holds the potential to revolutionize brain-machine interfaces (BMIs). This research explores the development of hardware and software systems that mimic the human brain's structure and function, aiming to enhance BMIs for various applications including prosthetics, neurorehabilitation, and neural prostheses. By leveraging neuro-morphic architectures, the study seeks to create more efficient, adaptive, and responsive interfaces that can interpret and respond to neural signals with greater precision and speed. The research evaluates the performance of these neuro-morphic systems in real-world scenarios, examining their effectiveness in improving the quality of life for individuals with neurological impairments. The findings will contribute to advancing the field of neuro-morphic computing and its applications in healthcare, paving the way for more natural and intuitive interactions between humans and machines.

Keyphrases: Adaptive Interfaces, Brain Machine Interfaces, Computer Science, Healthcare Technology, Microelectronics, Neural Signals, Neuro-morphic computing, Neurorehabilitation, Neuroscience, Prosthetics, neural prostheses

@booklet{EasyChair:14044,
  author    = {Kaledio Potter and Dylan Stilinki and Joseph Oluwaseyi},
  title     = {Neuro-Morphic Computing for Brain-Machine Interfaces},
  howpublished = {EasyChair Preprint 14044},
  year      = {EasyChair, 2024}}