Towards Closed-loop Deep Brain Stimulation: Behavior Recognition from Human STN

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). 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 side effects by adjusting stimulation parameters based on patient’s behavior. Thus 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. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses 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. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.

Identification of geostationary satellites using polarization data from unresolved images

In order to protect critical military and commercial space assets, the United States Space Surveillance Network must have the ability to positively identify and characterize all space objects. Unfortunately, positive identification and characterization of space objects is a manual and labor intensive process today since even large telescopes cannot provide resolved images of most space objects. Since resolved images of geosynchronous satellites are not technically feasible with current technology, another method of distinguishing space objects was explored that exploits the polarization signature from unresolved images. The objective of this study was to collect and analyze visible-spectrum polarization data from unresolved images of geosynchronous satellites taken over various solar phase angles. Different collection geometries were used to evaluate the polarization contribution of solar arrays, thermal control materials, antennas, and the satellite bus as the solar phase angle changed. Since materials on space objects age due to the space environment, it was postulated that their polarization signature may change enough to allow discrimination of identical satellites launched at different times. The instrumentation used in this experiment was a United States Air Force Academy (USAFA) Department of Physics system that consists of a 20-inch Ritchey-Chrétien telescope and a dual focal plane optical train fed with a polarizing beam splitter. A rigorous calibration of the system was performed that included corrections for pixel bias, dark current, and response. Additionally, the two channel polarimeter was calibrated by experimentally determining the Mueller matrix for the system and relating image intensity at the two cameras to Stokes parameters S0 and S1. After the system calibration, polarization data was collected during three nights on eight geosynchronous satellites built by various manufacturers and launched several years apart. Three pairs of the eight satellites were identical buses to determine if identical buses could be correctly differentiated. When Stokes parameters were plotted against time and solar phase angle, the data indicates that there were distinguishing features in S0 (total intensity) and S1 (linear polarization) that may lead to positive identification or classification of each satellite.