title: Motor task event detection using Subthalamic Nucleus Local Field Potentials.
abstract: Deep Brain Stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson's disease. Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and DBS side effects. In such systems, DBS parameters are adjusted based on patient's behavior, which means that behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local Field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. A practical behavior detection method should be able to detect behaviors asynchronously meaning that it should not use any prior knowledge of behavior onsets. In this paper, we introduce a behavior detection method that is able to asynchronously detect the finger movements of Parkinson patients. As a result of this study, we learned that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We used non-linear regression method to measure this connectivity and use it to detect the finger movements. Performance of this method is evaluated using Receiver Operating Characteristic (ROC).
authors.forename: Soroush
authors.initials: S
authors.lastname: Niketeghad
authors.forename: Adam O
authors.initials: AO
authors.lastname: Hebb
authors.forename: Joshua
authors.initials: J
authors.lastname: Nedrud
authors.forename: Sara J
authors.initials: SJ
authors.lastname: Hanrahan
authors.forename: Mohammad H
authors.initials: MH
authors.lastname: Mahoor
mesh_terms: D046690:Deep Brain Stimulation; D005385:Fingers; D006801:Humans; D009068:Movement; D010300:Parkinson Disease; D020531:Subthalamic Nucleus