A Multiple Kernel Learning Approach for Human Behavioral Task Classification using STN-LFP Signal

TL;DR

This paper introduces a multiple kernel learning method using SVMs to classify human behavioral tasks from STN-LFP signals, improving accuracy and efficiency for potential closed-loop DBS systems.

Contribution

It presents a novel MKL-based classification approach that outperforms single kernel methods in recognizing behavioral tasks from low-rate LFP signals.

Findings

MKL significantly outperforms single kernel SVM classifiers

Effective classification achieved with low sampling rate signals

Method applicable to multiple behavioral tasks

Abstract

Deep Brain Stimulation (DBS) has gained increasing attention as an effective method to mitigate Parkinsons disease (PD) disorders. Existing DBS systems are open-loop such that the system parameters are not adjusted automatically based on patients behavior. Classification of human behavior is an important step in the design of the next generation of DBS systems that are closed-loop. This paper presents a classification approach to recognize such behavioral tasks using the subthalamic nucleus (STN) Local Field Potential (LFP) signals. In our approach, we use the time-frequency representation (spectrogram) of the raw LFP signals recorded from left and right STNs as the feature vectors. Then these features are combined together via Support Vector Machines (SVM) with Multiple Kernel Learning (MKL) formulation. The MKL-based classification method is utilized to classify different tasks: button press, mouth movement, speech, and arm movement. Our experiments show that the lp-norm MKL significantly outperforms single kernel SVM-based classifiers in classifying behavioral tasks of five subjects even using signals acquired with a low sampling rate of 10 Hz. This leads to a lower computational cost.