基于FFT的STN-LFP信号同步人体行为识别方法
An FFT-based Synchronization Approach to Recognize Human Behaviors using STN-LFP Signal
摘要：
人类行为的分类是开发闭环脑深部电刺激（DBS）系统的关键，这可能能够降低现有系统的功耗和副作用。最近的研究表明，来自大脑底丘脑核（Subthalamic Nuclei，丘脑底核）的局部场电位（Local Field Potential，LFP）信号可以用于识别人类行为。由于植入每个脑电刺激器的DBS电极导线可以收集三个双极信号，因此选择一对合适的LFP以实现最佳识别性能仍然是一个有待解决的问题。考虑到基底神经节中存在同步聚集活动，本文提出了一种基于FFT的同步方法，自动选择一对相关的LFP，并将该对与基于SVM的MKL分类器一起用于行为识别目的。我们对五个主题的实验表明，所提出的方法相比，用于行为分类的其他方法的优越性。
英文摘要：
Classification of human behavior is key to developing closed-loop Deep Brain Stimulation (DBS) systems, which may be able to decrease the power consumption and side effects of the existing systems. Recent studies have shown that the Local Field Potential (LFP) signals from both Subthalamic Nuclei (STN) of the brain can be used to recognize human behavior. Since the DBS leads implanted in each STN can collect three bipolar signals, the selection of a suitable pair of LFPs that achieves optimal recognition performance is still an open problem to address. Considering the presence of synchronized aggregate activity in the basal ganglia, this paper presents an FFT-based synchronization approach to automatically select a relevant pair of LFPs and use the pair together with an SVM-based MKL classifier for behavior recognition purposes. Our experiments on five subjects show the superiority of the proposed approach compared to other methods used for behavior classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
