INTRODUCTION Modern complex systems are capable of placing both high and low levels of cognitive demands on their human operators. If operators do not maintain an optimal functional state, then overall system performance may suffer. On one hand, for example, if an operator is in a state of cognitive overload, system performance may degrade and the probability of making errors increases. As a result, when the suitability of new or modified systems is tested, accurate assessment of operator functional state is required. If the system places demands on the operator beyond his or her capabilities, then the system or operational procedures must be modified. On the other hand, high levels of automation can lead to complacency, which may result in the operator losing awareness of the system's true state. This situation can also lead to reduced performance and increased errors. In some circumstances it may be possible for the system to adapt the task to meet the momentary needs of the operator (Byrne & Parasuraman, 1996). For example, if the operator is becoming mentally overloaded, the system may be able to automate some aspects of the task to reduce the cognitive demands on the operator. The reduced task demands should lead to reduced operator mental workload and enhanced performance. Accurate estimations of operator functional state are crucial in these situations. One method of assessing operator functional state employs the electrophysiological signals emitted by the human body. These signals are known to change in characteristic ways as the state of the operator changes. For example, the electroencephalograph (EEG) displays characteristic patterns when an operator is performing a task. Heart rate increases as the cognitive demands on the operator increase. The rate of eye blinking decreases when the visual demands of a task increase (Kramer, 1991; Wilson & Eggemeier, 1991). Psychophysiological signals are additionally attractive because they are continuously available and their collection does not interfere with the operator's job performance. Previous research has shown that classifiers using physiological features can correctly determine the level of cognitive activity in tasks having differing levels of demand. However, these results have to be expanded before these procedures can be applied to the complex tasks found in the workplace. Prior studies used either simple, single-task paradigms (Gevins et al., 1998; Nikolaev, Ivanitskii, & Ivanitskii, 1998; Wilson & Fisher, 1995) or relatively few physiological variables with complex task performance (Wilson & Fisher, 1991). Gevins et al. (1998) used a hybrid statistical and artificial neural network (ANN) procedure to classify two forms of a working memory task having three levels of difficulty. Using 27 channels of EEG, they reported classifying task difficulty with a mean correct classification of 88.7%. Nikolaev et al. (1998) used data from 14 EEG channels to discriminate between spatial and logical cognitive tasks with an ANN. They reported an overall correct classification of 86%. Wilson and Fisher (1995) utilized 19 channels of EEG with a stepwise discriminant analysis (SWDA) classifier to discriminate between two levels of task difficulty in seven different laboratory tasks. They achieved an overall mean correct classification of 86%. Heart rate and eye blink data recorded from pilots and weapon systems officers during air-to-ground training flights were used by Wilson and Fisher (1991). With SWDA as their classifier, they reported 92% accuracy in correctly classifying eight segments of flight for the pilots and 89% for the weapon systems officers. Overall, the classification accuracy for these studies is very high and comparable, even though diverse tasks and classifiers were used. Physiological data, primarily EEG, have been used in medicine to classify categories of neuropathy and psychiatric disorders (John, Pricep, Fridman, & Easton, 1988). …
['Glenn F. Wilson', 'Christopher A. Russell']
Operator functional state classification using multiple psychophysiological features in an air traffic control task.