abstract: Parkinson's disease is a neurodegenerative condition diagnosed on patients with clinical history and motor signs of tremor, rigidity and bradykinesia, and the estimated number of patients living with Parkinson's disease around the world is seven to ten million. Deep brain stimulation (DBS) provides substantial relief of the motor signs of Parkinson's disease patients. It is an advanced surgical technique that is used when drug therapy is no longer sufficient for Parkinson's disease patients. DBS alleviates the motor symptoms of Parkinson's disease by targeting the subthalamic nucleus using high-frequency electrical stimulation. This work proposes a behavior recognition model for patients with Parkinson's disease. In particular, an adaptive learning method is proposed to classify behavioral tasks of Parkinson's disease patients using local field potential and electrocorticography signals that are collected during DBS implantation surgeries. Unique patterns exhibited between these signals in a matched feature space would lead to distinction between motor and language behavioral tasks. Unique features are first extracted from deep brain signals in the time-frequency space using the matching pursuit decomposition algorithm. The Dirichlet process Gaussian mixture model uses the extracted features to cluster the different behavioral signal patterns, without training or any prior information. The performance of the method is then compared with other machine learning methods and the advantages of each method is discussed under different conditions. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015