Single trial behavioral task classification using subthalamic nucleus local field potential signals
Deep Brain Stimulation (DBS) has been a successful technique for alleviating Parkinson's disease (PD) symptoms especially for whom drug therapy is no longer efficient. Existing DBS therapy is open-loop, providing a time invariant stimulation pulse train that is not customized to the patient's current behavioral task. By customizing this pulse train to the patient's current task the side effects may be suppressed. This paper introduces a method for single trial recognition of the patient's current task using the local field potential (LFP) signals. This method utilizes wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. These algorithms will be applied in a closed loop feedback control system to optimize DBS parameters to the patient's real time behavioral goals.