  This paper presents the results of our recent work on studying the effects of
deep brain stimulation (DBS) and medication on the dynamics of brain local
field potential (LFP) signals used for behavior analysis of patients with
Parkinson s disease (PD). DBS is a technique used to alleviate the severe
symptoms of PD when pharmacotherapy is not very effective. Behavior recognition
from the LFP signals recorded from the subthalamic nucleus (STN) has
application in developing closed-loop DBS systems, where the stimulation pulse
is adaptively generated according to subjects performing behavior. Most of the
existing studies on behavior recognition that use STN-LFPs are based on the DBS
being off. This paper discovers how the performance and accuracy of automated
behavior recognition from the LFP signals are affected under different
paradigms of stimulation on/off. We first study the notion of beta power
suppression in LFP signals under different scenarios (stimulation on/off and
medication on/off). Afterward, we explore the accuracy of support vector
machines in predicting human actions (button press and reach) using the
spectrogram of STN-LFP signals. Our experiments on the recorded LFP signals of
three subjects confirm that the beta power is suppressed significantly when the
patients take medication (p-value<0.002) or stimulation (p-value<0.0003). The
results also show that we can classify different behaviors with a reasonable
accuracy of 85% even when the high-amplitude stimulation is applied.
