Electroencephalography (EEG) has been used for over 80 years to monitor brain activity. The basic technology of using electrodes placed on the scalp with conductive gel or paste (“wet electrodes”) has not fundamentally changed in that time. An electrode system that does not require conductive gel and skin preparation represents a major advancement in this technology and could significantly increase the utility of such a system for many human factors applications. QUASAR, Inc. (San Diego, CA) has developed a prototype dry electrode system for EEG that may well deliver on the promises of dry electrode technology; before any such system could gain widespread acceptance, it is essential to directly compare their system with conventional wet electrodes. An independent validation of dry vs. wet electrodes was conducted; in general, the results confirm that the data collected by the new system is comparable to conventional wet technology.

No abstract

Combined psychophysiological measures have been used to determine mental workload in operators, but the day-today reliability of these measures has not been determined. Data were collected four times over a one month period. Two classifiers were trained with these data and their ability to correctly discriminate between two levels of task difficulty with new data was tested. Both classifiers very accurately discriminated between the two levels of task difficulty using data collected on the same day as the training data. However, the accuracy was considerably reduced when tested on data from days different from the training data. The implications for application of these procedures are discussed.

No abstract

Eye movements and pupil size have been used to assess workload in previous research. However, the results presented in the literature vary, and the tasks have been too simple at times or the experimental conditions (e.g. lighting) too tightly controlled to determine if the use of eye data to assess workload is useful in real-world contexts. This research investigates the use of ten eye movement, eyelid, or pupil related metrics as input to support vector machines for classifying workload in a complex task. The results indicate that both pupil size and percentage of eye closure are useful for predicting workload. Further, the combination of the two metrics increases the robustness and accuracy of the workload predictions.