Adaptive automation, a novel neuroergonomic concept, refers to a human-machine system that uses real-time assessment of the operator's workload to make the necessary changes to enhance performance. For adaptive automation to work, the system must utilize an accurate operator-state classifier for the real-time assessment. Operator-state classifiers such as discriminant analysis and artificial neural networks show an accuracy of 70% to 85% in real-time. An important part to properly implementing adaptive automation is figuring out how big a workload needs to be to require intervention. Implementing neuroergonomic adaptive automation would require the development of nonintrusive sensors and even techniques to track eye movement. Current research into assessing a person's mental state includes using facial electromyography to detect confusion.