  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.
