Academic journal article Human Factors

Monitoring Task Loading with Multivariate EEG Measures during Complex Forms of Human-Computer Interaction

Academic journal article Human Factors

Monitoring Task Loading with Multivariate EEG Measures during Complex Forms of Human-Computer Interaction

Article excerpt


Electroencephalographic (EEG) recordings were made while 16 participants performed versions of a personal-computer-based flight simulation task of low, moderate, or high difficulty. As task difficulty increased, frontal midline theta EEG activity increased and alpha band activity decreased. A participant-specific function that combined multiple EEG features to create a single load index was derived from a sample of each participant's data and then applied to new test data from that participant. Index values were computed for every 4 s of task data. Across participants, mean task load index values increased systematically with increasing task difficulty and differed significantly between the different task versions. Actual or potential applications of this research include the use of multivariate EEG-based methods to monitor task loading during naturalistic computer-based work.


This paper documents the development and evaluation of a neurophysiology-based method for deriving a continuous index of task loading from individuals engaged in complex computer-based work. Task loading is used here to refer to the degree to which neural resources are activated by effortful task performance. Because a human operator's functional capacity is limited and can often be exceeded in work environments that demand sustained vigilance to multiple streams of information, the likelihood of performance errors can be high (Card, Moran, & Newell, 1983). The ability to monitor task loading continuously might thus be valuable in task analysis research, in efforts to improve the usability of human-computer interfaces (Raskin, 2000), and in efforts to design appropriate adaptive automation strategies (Byme & Parasuraman, 1996; Morrison & Gluckman, 1994; Parasuraman, Sheridan, & Wickens, 2000).

Although both subjective estimates and assessment of overt behavioral performance can permit detailed inferences about task loading, other modalities might also provide convergent insights into the degree to which a task environment is demanding of neural resources. To be useful in applied contexts, a monitoring method must be robust enough to be reliably measured under relatively unstructured task conditions and sensitive enough to consistently vary with some dimension of interest. Furthermore, it should not interfere with operator performance, it should be applicable across many contexts, and it should have reasonably good time resolution. Some physiological measurements - particularly measures of central nervous system activity, such as the electroencephalogram (EEG) - appear to meet these requirements.

EEG and event-related potential measures have been shown to be fairly sensitive to variations in task difficulty (Gevins, Smith, McEvoy, & Yu, 1997; Humphrey & Kramer, 1994; McCallum, Cooper, & Pocock, 1988; Parasuraman, 1990). In a recent study, Gevins et al. (1998) demonstrated that EEG-based, participant-specific, multivariate pattern recognition methods could be used to discriminate levels of task loading under highly controlled task conditions. In that study, continuous EEG data were recorded from 8 healthy adults performing a simple task in which working memory demands were varied in order to modulate task difficulty. As task difficulty increased, performance accuracy decreased, response times increased, and a change in the power of the EEG spectra in the theta (5-7 Hz) and alpha (8-12 Hz) frequency bands occurred. In particular, during the highest task load there was an increase in theta power over frontal regions of the scalp and a decrease in alpha power over widespread scalp regions, relative to me asures taken during the lowest task load. Two-class, neural-network-based pattern recognition functions applied to EEG spectral features reliably distinguished low load from high load and moderate load from low or high load.

Such results suggest that with further development, it might become possible to use EEG-based methods for unobtrusively monitoring task loading in individuals engaged in computer-based work. …

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