Glenn F. Wilson

Psychophysiological responses to changes in workload during simulated air traffic control.

In this investigation, eight Air Force air traffic controllers (ATCs) performed three scenarios on TRACON (Terminal Radar Approach Control), a computer-based air traffic control (ATC) simulation. Two scenarios were used each with three levels of difficulty. One scenario varied traffic volume by manipulating the number of aircraft to be handled and the second scenario varied traffic complexity by manipulating arriving to departing flight ratios, pilot skill and mixture of aircraft types. A third scenario, overload, required subjects to handle a larger number of aircraft in a limited amount of time. The effects of the manipulations on controller workload were assessed using performance, subjective (TLX), and physiological (EEG, eye blink, heart rate, respiration, saccade) measures. Significant main effects of difficulty level were found for TRACON performance, TLX, eye blink, respiration and EEG measures. Only the EEG was associated with main effects for the type of traffic. The results provide support for the differential sensitivity of a variety of workload measures in complex tasks, underscore the importance of traffic complexity in ATC workload, and support the utility of TRACON as a tool for studies of ATC workload.

Removal of ocular artifacts from electro-encephalogram by adaptive filtering

The electro-encephalogram (EEG) is useful for clinical diagnosts and in biomedical research. EEG signals, however, especially those recorded from frontal channels, often contain strong electro-oculogram (EOG) artifacts produced by eye movements. Existing regression-based methods for removing EOG artifacts require various procedures for preprocessing and calibration that are inconvenient and timeconsuming. The paper describes a method for removing ocular artifacts based on adaptive filtering. The method uses separately recorded vertical EOG and horizontal EOG signals as two reference inputs. Each reference input is first processed by a finite impulse response filter of length M (M=3 in this application) and then subtracted from the original EEG. The method is implemented by a recursive leastsquares algorithm that includes a forgetting factor (λ=0.9999 in this application) to track the non-stationary portion of the EOG signals. Results from experimental data demonstrate that the method is easy to implement and stable, converges fast and is suitable for on-line removal of EOG artifacts. The first three coefficients (up to M=3) were significantly larger than any remaining coefficients.

Performance Enhancement in an Uninhabited Air Vehicle Task Using Psychophysiologically Determined Adaptive Aiding

Objective: We show that psychophysiologically driven real-time adaptive aiding significantly enhances performance in a complex aviation task. Afurther goal was to assess the importance of individual operator capabilities when providing adaptive aiding. Background: Psychophysiological measures are useful for monitoring cognitive workload in laboratory and real-world settings. They can be recorded without intruding into task performance and can be analyzed in real time, making them candidates for providing operator functional state estimates. These estimates could be used to determine if and when system intervention should be provided to assist the operator to improve system performance. Methods: Adaptive automation was implemented while operators performed an uninhabited aerial vehicle task. Psychophysiological data were collected and an artificial neural network was used to detect periods of high and low mental workload in real time. The high-difficulty task levels used to initiate the adaptive automation were determined separately for each operator, and a group-derived mean difficulty level was also used. Results: Psychophysiologically determined aiding significantly improved performance when compared with the no-aiding conditions. Improvement was greater when adaptive aiding was provided based on individualized criteria rather than on group-derived criteria. The improvements were significantly greater than when the aiding was randomly provided. Conclusion: These results show that psychophysiologically determined operator functional state assessment in real time led to performance improvement when included in closed loop adaptive automation with a complex task. Application: Potential future applications of this research include enhanced workstations using adaptive aiding that would be driven by operator functional state.

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.

Putting the Brain to Work : Neuroergonomics Past, Present, and Future

Objective: The authors describe research and applications in prominent areas of neuroergonomics. Background: Because human factors/ergonomics examines behavior and mind at work, it should include the study of brain mechanisms underlying human performance. Methods: Neuroergonomic studies are reviewed in four areas: workload and vigilance, adaptive automation, neuroengineering, and molecular genetics and individual differences. Results: Neuroimaging studies have helped identify the components of mental workload, workload assessment in complex tasks, and resource depletion in vigilance. Furthermore, real-time neurocognitive assessment of workload can trigger adaptive automation. Neural measures can also drive brain-computer interfaces to provide disabled users new communication channels. Finally, variants of particular genes can be associated with individual differences in specific cognitive functions. Conclusions: Neuroergonomics shows that considering what makes work possible — the human brain — can enrich understanding of the use of technology by humans and can inform technological design. Application: Applications of neuroergonomics include the assessment of operator workload and vigilance, implementation of real-time adaptive automation, neuroengineering for people with disabilities, and design of selection and training methods.

Psychophysiological Measures of Workload during Continuous Manual Performance

Twelve subjects (six female) participated in an experiment designed to separate the effects of perceptual/central and physical demands on psychophysiological measures of peripheral nervous system activity. The difficulty of a single-axis continuous manual tracking task was varied in two ways: order of control was manipulated to vary perceptual/central processing demand, and disturbance amplitude was manipulated to vary physical demand. Physiological measures were sensitive to the imposition of a task and were more sensitive to physical than to perceptual/central demands. A principal components analysis identified five factors (three of them physiological) that accounted for 83.1% of the observed variance. Perceptual/central processing demands specifically affected the component identified with sympathetic cardiovascular control, whereas physical demands were reflected in the component identified with parasympathetic cardiovascular control. This finding suggests that dissociations observed among cardiovascular measures in manual performance tasks are attributable to differential activation of the autonomic control systems.

Physiological Data Used to Measure Pilot Workload in Actual Flight and Simulator Conditions

Three physiological measures of workload; heart rate, eye blink, and EEG were recorded from eight experienced A-7 attack aircraft pilots. Each pilot flew the same familiar training mission three times; one mission in the lead position of a four ship formation and the other as wing, and once in an A-7 simulator. The mission lasted approximately 90 minutes and consisted of take-off, low altitude terrain following, high G maneuvers, inflight navigational updates, weapons delivery, and a high altitude cruise to base, ending in a formation landing. The data show significant differences between simulated and actual flights for all measures. There were also significant differences between mission segments for each pilot. The heart rate data most obviously reflect the changes in workload level throughout the mission and between flight position and simulator. Blink rate and duration were sensitive to changing visual attentional demands. The EEG data showed differences between the actual flight missions and the simulator.

Removal of Ocular Artifacts from EEG: A Comparison of Adaptive Filtering Method and Regression Method Using Simulated Data

We recently proposed an adaptive filtering method for removing ocular artifacts from EEG recordings. In this study, the accuracy of this method is evaluated quantitatively using simulated data and compared with the accuracy of the time domain regression method. The results show that when transfer of ocular signal to EEG channel is frequency dependent, or when there is a time delay, the adaptive filtering method is more accurate in recovering the true EEG signals

A new EOG-based eyeblink detection algorithm

Accurate and efficient operator functional state classification and assessment based on physiological data have many important applications ranging from operator monitoring to interaction and control of human/machine systems. Eyeblink characteristics are frequently used as physiological indicators for this purpose. In this paper, we describe an efficient and robust eyeblink detection algorithm based on nonlinear analysis of the electrooculogram (EOG) signal. The performance of the algorithm was evaluated via data analysis results of several benchmark test sets in comparison with another eyeblink detection algorithm.

Hemispheric Asymmetries in Phonological Processing Assessed with Probe Evoked Magnetic Fields

Auditory Evoked Magnetic Fields (EFs) to tonal stimuli were recorded at homotopic maxima over the left and right auditory areas in nine subjects. Recordings were made during two conditions, both involving simultaneous presentation of the probe tone stimuli and a set of tape-recorded verbal material. During the control condition subjects were instructed to attend to the tones and ignore the verbal material. In the phonological processing condition they were instructed to ignore the tones and attempt to identify a phonological target item which was embedded in the verbal material. EFs obtained during both conditions were characterized by an early N1m and a later P2m component corresponding to the N1 and P2 components of auditory evoked potentials (EPs). During the phonological condition, the amplitude of the N1m was significantly reduced in both hemispheres symmetrically whereas the amplitude of the P2m was attenuated to a significantly greater degree in the left hemisphere. These data are in agreement with previous EP evidence of greater interference of linguistic processing with processing of irrelevant probe stimuli in the left hemisphere, indicative of greater left hemisphere involvement in language tasks.