An FFT-based Synchronization Approach to Recognize Human Behaviors using STN-LFP Signal

TL;DR

This paper introduces an FFT-based synchronization method to select optimal LFP signal pairs from STN for human behavior recognition, enhancing classification accuracy in DBS systems.

Contribution

It proposes a novel FFT-based synchronization approach for automatic LFP pair selection and combines it with SVM-MKL classification for improved behavior recognition.

Findings

Proposed method outperforms existing classification techniques.

Effective automatic selection of LFP pairs improves recognition accuracy.

Validated on data from five subjects.

Abstract

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.