In this study, we analyze electrocorticogram (ECoG) and local field potential (LFP) signals of patients diagnosed with Parkinson’s disease (PD) in order to observe the effects of the disease on brain activity. The LFP signals are recorded from deep brain stimulation (DBS) electrodes inserted into the GPe and GPi structures in the basal ganglia. DBS aims to lessen the motor symptoms of PD. Each signal consists of data recorded while the patient was at rest and then in movement which consisted of left hand movement. Research suggests that the motor symptoms of PD correlate with beta activity in the brain, which includes frequencies of about 13 - 30 Hz. We use an averaged power spectrum to visualize the areas of most activity. It is also hypothesized that there exist low beta and high beta subbands that range between 13 - 20 Hz and 20 - 30 Hz, respectively. We test whether or not this hypothesis holds for all acquired signals in our data set. In addition, we consider the amplitude of the signals in the beta band in order to study the difference in amplitude of the signal in rest and movement states. We begin by using the Fourier transform along with a scale-space boundary detection method in order to test whether or not the signal exhibits most behavior in the beta range. From the boundary detection, we see that the number of subbands could vary from patient to patient. More importantly, even with the supposition of only two subbands, the end and start of each subband is up for debate. Each patient displayed a slightly different range, encouraging the need for patient driven methods. We then use an empirical wavelet transform (EWT) to extract the instantaneous frequencies and amplitudes of the signals in the beta range. The EWT provides us with a few results that also support the idea of activity in the beta frequency band.