Probe-independent EEG assessment of mental workload in pilots

Abstract

Existing approaches for quantifying mental workload using electroencephalography often rely on probe stimuli to elicit stereotyped neural responses such as the P300 wave. Here we explore probe-independent algorithms for classifying three levels of task-complexity in a flight simulator experiment. Using input features derived from estimates of the average power in five frequency bands, we test a variety of classifiers, using 10-fold cross-validation to estimate test set error. Classification accuracy was above 50% (chance performance: 33.33%) in 13 of 20 subjects on at least one of the four recorded channels, and reached as high as 87.35%. There was strong variability across subjects in both the strength and direction of the relationships between the input features and task-complexity labels, suggesting that classifiers using these input features must be trained to the individual to be useful.