Neuroscience and neurotechnology are currently being revolutionized by artificial intelligence (AI) and machine learning. AI is widely used to study and interpret neural signals (analytical applications), assist people with disabilities (prosthetic applications), and treat underlying neurological symptoms (therapeutic applications). In this brief, we will review the emerging opportunities of on-chip AI for the next-generation implantable brain-machine interfaces (BMIs), with a focus on state-of-the-art prosthetic BMIs. Major technological challenges for the effectiveness of AI models will be discussed. Finally, we will present algorithmic and IC design solutions to enable a new generation of AI-enhanced and high-channel-count BMIs.
@article{arxiv.2204.02362,
title = {Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs},
author = {MohammadAli Shaeri and Arshia Afzal and Mahsa Shoaran},
journal= {arXiv preprint arXiv:2204.02362},
year = {2022}
}