An FFT-based Synchronization Approach to Recognize Human Behaviors using STN-LFP Signal
This work addresses a specific bottleneck in developing closed-loop Deep Brain Stimulation systems to reduce power consumption and side effects, representing an incremental improvement in signal selection for behavior classification.
The paper tackles the problem of selecting an optimal pair of Local Field Potential (LFP) signals from the brain's Subthalamic Nuclei for human behavior recognition, using an FFT-based synchronization approach combined with an SVM-based MKL classifier, and demonstrates its superiority over other methods in experiments on five subjects.
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.