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.

Ethnic Violence on Kenya's Periphery: Informal Institutions and Local Resilience in Conflict-Affected Communities

Deadly, inter-ethnic group conflict remains a threat to international security in a world where the majority of armed violence occurs not only within states but in the most ungoverned areas within states. Conflicts that occur between groups living in largely ungoverned areas often become deeply protracted and are difficult to resolve when the state is weak and harsh environmental conditions place human security increasingly under threat. However, even under these conditions, why do some local conflicts between ethnic groups escalate, whereas others do not? To analyze this puzzle, the dissertation employs comparative methods to investigate the conditions under which violence erupts or stops and armed actors choose to preserve peace. The project draws upon qualitative data derived from semi-structured interviews, focus group dialogues, and participant observation of local peace processes during field research conducted in six conflict-affected counties in Northern Kenya. Comparative analysis of fifteen conflict episodes with variable outcomes reveals the conditions under which coalitions of civic associations, including local peace committees, faith-based organizations, and councils of elders, inter alia, enhance informal institutional arrangements that contain escalation. Violence is less likely to escalate in communities where cohesive coalitions provide platforms for threat-monitoring, informal pact making, and enforcement of traditional codes of restitution. However, key scope conditions affect whether or not informal organizational structures are capable of containing escalation. In particular, symbolic acts of violence and the use of indiscriminant force by police and military actors commonly undermine local efforts to contain conflict. The dissertation contributes to the literatures on civil society and peacebuilding, demonstrating the importance of comparing processes of escalation and non-escalation and accounting for interactive effects between modes of state and non-state response to local, inter-ethnic group conflict.