Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs
This addresses the need for advanced implantable BMIs to assist people with disabilities, but it is incremental as it reviews existing challenges and solutions without presenting new experimental results.
The paper reviews the potential of on-chip AI for next-generation implantable brain-machine interfaces (BMIs), focusing on prosthetic applications, and discusses technological challenges and solutions to enable AI-enhanced, high-channel-count BMIs.
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.