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.