计算机科学
脑-机接口
人工智能
光学(聚焦)
神经工程
人工神经网络
机器学习
GSM演进的增强数据速率
深度学习
脑植入物
人机交互
神经科学
心理学
物理
脑电图
光学
作者
MohammadAli Shaeri,Arshia Afzal,Mahsa Shoaran
标识
DOI:10.1109/aicas54282.2022.9870008
摘要
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 (ther-apeutic 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.
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