Biomedical subjects

Glenn F Wilson

Publications and source records attributed to Glenn F Wilson.

3 recordsLinked to original sources

PubMed2005

EEG correlates of G-induced loss of consciousness.

INTRODUCTION: Electroencephalographic (EEG) correlates of Gz-induced loss of consciousness (G-LOC) were examined to better understand the mechanisms of G-LOC. METHODS: There were 10 subjects (4 women) who provided 35 G-LOC episodes during which 13 channels of EEG and eye activity were recorded. Subjects simultaneously performed tracking and mathematical tasks prior to and following G-LOC. The performance data and cerebral regional oxygen saturation (rSO2), as measured using near infrared spectroscopy, were correlated with the EEG changes. RESULTS: Across all subjects, seven EEG events were identified and measured. The two most significant were found over widespread scalp sites. They were a 1-2 Hz waveform just prior to G-LOC and a very large, approximately 1 Hz waveform just prior to regaining consciousness. These were associated with a drop of rSO2 levels to approximately 90% of the pre-G-LOC baseline levels. rSO2 levels returned to pre-G-LOC levels within approximately 15 s following G-LOC while EEG and performance measures took approximately 60 s. DISCUSSION: There were two unique EEG waveforms found, one preceding unconsciousness and the other preceding the return to consciousness during G-LOC episodes. Further, the supply of oxygen to the brain was rapidly restored, within approximately 15 s, while the EEG and performance data showed that approximately 60 s were required for the brain to regain functional integrity. The delay in performance and EEG recovery following G-LOC demonstrates that the recovery of brain activity to support cognitive function requires more than just the restoration of normal oxygen levels.

Adult↗

PubMed2003

Operator functional state classification using multiple psychophysiological features in an air traffic control task.

We studied 2 classifiers to determine their ability to discriminate among 4 levels of mental workload during a simulated air traffic control task using psychophysiological measures. Data from 7 air traffic controllers were used to train and test artificial neural network and stepwise discriminant classifiers. Very high levels of classification accuracy were achieved by both classifiers. When the 2 task difficulty manipulations were tested separately, the percentage correct classifications were between 84% and 88%. Feature reduction using saliency analysis for the artificial neural networks resulted in a mean of 90% correct classification accuracy. Considering the data as a 2-class problem, acceptable load versus overload, resulted in almost perfect classification accuracies, with mean percentage correct of 98%. In applied situations, the most important distinction among operator functional states would be to detect mental overload situations. These results suggest that psychophysiological data are capable of such discriminations with high levels of accuracy. Potential applications of this research include test and evaluation of new and modified systems and adaptive aiding.

Adult↗

PubMedDate not supplied

Real-time assessment of mental workload using psychophysiological measures and artificial neural networks.

The functional state of the human operator is critical to optimal system performance. Degraded states of operator functioning can lead to errors and overall suboptimal system performance. Accurate assessment of operator functional state is crucial to the successful implementation of an adaptive aiding system. One method of determining operators' functional state is by monitoring their physiology. In the present study, artificial neural networks using physiological signals were used to continuously monitor, in real time, the functional state of 7 participants while they performed the Multi-Attribute Task Battery with two levels of task difficulty. Six channels of brain electrical activity and eye, heart and respiration measures were evaluated on line. The accuracy of the classifier was determined to test its utility as an on-line measure of operator state. The mean classification accuracies were 85%, 82%, and 86% for the baseline, low task difficulty, and high task difficulty conditions, respectively. The high levels of accuracy suggest that these procedures can be used to provide accurate estimates of operator functional state that can be used to provide adaptive aiding. The relative contribution of each of the 43 psychophysiological features was also determined. Actual or potential applications of this research include test and evaluation and adaptive aiding implementation.

Adult↗