神经调节
计算机科学
大脑研究
GSM演进的增强数据速率
人机交互
人工智能
神经科学
认知科学
数据科学
心理学
刺激
作者
Yan Li,Yingnan Nie,Zhaoyu Quan,Han Zhang,Rui Song,Hao Feng,Xi Cheng,Wei Liu,Xinyi Geng,Xinwei Sun,Yanwei Fu,Shouyan Wang
出处
期刊:Heliyon
[Elsevier]
日期:2024-06-01
卷期号:10 (12): e32609-e32609
标识
DOI:10.1016/j.heliyon.2024.e32609
摘要
Closed-loop neuromodulation with intelligence methods has shown great potentials in providing novel neuro-technology for treating neurological and psychiatric diseases. Development of brain-machine interactive neuromodulation strategies could lead to breakthroughs in precision and personalized electronic medicine. The neuromodulation research tool integrating artificial intelligent computing and performing neural sensing and stimulation in real-time could accelerate the development of closed-loop neuromodulation strategies and translational research into clinical application. In this study, we developed a brain-machine interactive neuromodulation research tool (BMINT), which has capabilities of neurophysiological signals sensing, computing with mainstream machine learning algorithms and delivering electrical stimulation pulse by pulse in real-time. The BMINT research tool achieved system time delay under 3 ms, and computing capabilities in feasible computation cost, efficient deployment of machine learning algorithms and acceleration process. Intelligent computing framework embedded in the BMINT enable real-time closed-loop neuromodulation developed with mainstream AI ecosystem resources. The BMINT could provide timely contribution to accelerate the translational research of intelligent neuromodulation by integrating neural sensing, edge AI computing and stimulation with AI ecosystems.
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