Eileen Krepkovich, BS

Education 

Research Interests

My research addresses several issues that arise from the development of neural-machine interfaces. The term neural-machine interface (NMI) refers to devices that link physical or neurological signals to patient intent for movement or communication augmentation. NMIs may need information extraction from multiple signal sources (e.g. multi-electrode arrays, implanted electromyogram [EMG] electrodes) and may detect intent for multidimensional control (e.g. 3D reaching). This leads to a multiple-input, multiple-output (MIMO) system. MIMO systems are computationally complex, and large numbers of inputs results in large computational burden. By reducing input number, we can improve computational/communication efficiency, algorithm performance, and practicality of use.

The goals of this study are twofold: to extend previous input selection algorithms to systems with multiple outputs, so that they can be applied for interpretation of multiple movements; and to evaluate how the algorithm performs on systems lacking explicit output, so they can be applied in disabled patients who can only ‘imagine’ movements instead of physically performing them. Experimental methods for testing algorithm performance on a MIMO system include recording EMGs and joint angle motion data while healthy subjects perform simple arm movements guided by a visual cueing system. To replicate situations where patients lack explicit output, the prediction of subjects’ actual joint angles are compared with prediction of standard joint angles (as calculated by an original subject performing the same movements). Additionally, we are examining the effects of varying movement complexity on algorithm performance by analyzing single joint movements as well as whole limb reaching patterns.

Contact Information

Department of Biomedical Engineering
Northwestern University
Rehabilitation Institute of Chicago, Suite 1409

Phone: 312-238-1685

e-romito@northwestern.edu