Deep Learning in Neuroprosthetics: Improving the Precision and Responsiveness of Brain-Machine Interfaces

Abstract

Deep learning has emerged as a transformative technology in the field of neuroprosthetics, significantly enhancing the precision and responsiveness of brain-machine interfaces (BMIs). These advanced computational models excel at decoding complex neural signals, allowing for more accurate and fluid control of prosthetic devices. Unlike traditional methods, deep learning models can process high-dimensional data from the brain, adapt to individual users, and facilitate real-time responses. This paper explores the latest advancements in applying deep learning techniques to neuroprosthetics, highlighting their role in improving neural decoding, sensory feedback, and closed-loop control systems. By delving into the current state of the art and discussing future prospects, we aim to demonstrate how deep learning is reshaping neuroprosthetics and moving the field toward more natural and intuitive prosthetic use.