Trent A. Skidmore

The Evoked Potential Human-computer Interface

This paper presents experimental evidence to support the feasibility of an evoked potential vision-tracking system. The topics discussed are stimulator construction, verification of the photic driving response in the electroencephalogram, and a method for performing frequency separation. The final issue considered is that of object multiplicity (concurrent visual stimuli with different flashing rates). The paper concludes by listing several applications currently under investigation.

JPALS Visualization Tool

This paper describes a visualization system for the Joint Precision Aircraft Landing System (JPALS) that integrates remote sensor data with well-known databases of terrain imagery, elevation, and surveyed aviation locations. This visualization system allows teams of research engineers and managers to collaborate in real-time, during test flights, in a distributed fashion. Each user interacts with their own copy of the virtual world, updated constantly in real-time, to manipulate and investigate in a fashion that best accomplishes their own specific task. This allows the entire team to collaborate simultaneously, which mitigates the common latency associated with team members performing offline tasks, such as inspecting log files or verifying MatLab plots. Test flights utilizing our visualization system have shown that it enabled the research team to make decisions on the fly, such as changing the antenna type and placement, and also allowed post-flight analysis and prediction of desirable changes. It could, for instance, predict optimal antenna placement for a given flight path. This can save a project the large expense of performing additional test flights if analysis had been performed offline between flights. Since our system is GPS based, it can be used as an alternate truth system to validate traditional instrument approach and landing systems. The recording and playback features of our visualization can also be used as a teaching aid in flight training for both pilots and air traffic controllers.

Analyzing Thought-related Electroencephalographic Data Using Nonlinear Techniques

A unique method is presented for collecting, studying and interpreting thought-related electroencephalogram (EEG) data. The use of a chaos based nonlinear analysis technique is shown to be promising in providing insight into relating conscious thought to specific EEG data. A discussion of the practical limitations of this technique is also included.