Evaluating Invasive EEG Implantations with Structural Imaging Data and Functional Scalp EEG Recordings from Epilepsy Patients.

Seizures in patients with medically refractory epilepsy (MRE) cannot be controlled with drugs. For focal MRE, seizures originate in the epileptogenic zone (EZ), which is the minimum amount of cortex that must be treated to be seizure free. Localizing the EZ is often a laborious process wherein clinicians first inspect scalp EEG recordings during several seizure events, and then formulate an implantation plan for subsequent invasive monitoring. The goal of implantation is to place electrodes into the brain region covering the EZ. Then, during invasive monitoring, clinicians visually inspect intracranial EEG recordings to more precisely localize the EZ. Finally, the EZ is then surgically ablated, removed or treated with electrical stimulation. Unfortunately success rates average at 50%. Such grim outcomes call for analytical assistance in creating more accurate implantation plans from scalp EEG. In this paper, we introduce a method that combines imaging data (CT and MRI scans) with scalp EEG to derive an implantation distribution. Specifically, scalp EEG data recorded over a seizure event is converted into a time-gamma frequency map, which is then processed to derive a spectrally annotated implantation distribution (SAID). The SAID represents a distribution of gamma power in each of eight cortical lobe/hemisphere partitions. We applied this method to 4 MRE patients who underwent treatment, and found that the SAID distribution overlapped more with clinical implantations in success cases than in failed cases. These preliminary findings suggest that the SAID may help in improving EZ localization accuracy and surgical outcomes.

Designing Feedback Controllers for Human-Prosthetic Systems Using H∞ Model Matching.

Prosthetic hands are important tools for improving the lives of upper limb amputees, yet most devices lack the ability to provide a sense of touch back to the user. Recent improvements have been made in electromyography (EMG) prosthesis control as well as in biologically relevant tactile sensors to provide sensory feedback to amputees through nerve stimulation. However, sensory feedback has been designed heuristically, which can lead to either unnatural sensations or to excessive feedback that bothers the user. In this study, we apply optimal control techniques to synthesize sensory feedback to the user, and to synthesize the conversion from EMG to an actuation command to the prosthesis. Specifically, we construct a feedback control system architecture and solve the $H_{\infty }$ model matching problem to make the closed-loop user-prosthetic system to behave like a pre-specified ideal system in response to elemental inputs (e.g. impulse, step, etc). We design feedback controllers assuming that human and prosthetic components behave in a linear fashion as a proof-of-concept, and the closed-loop system is able to match ideal systems that are slow, fast and that have both slow and fast dynamics (like healthy humans).