Using mental load for managing interruptions in physiologically attentive user interfaces
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Feasibility and pragmatics of classifying working memory load with an electroencephalograph
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Simulation fidelity design informed by physiologically-based measurement tools
FAC'07 Proceedings of the 3rd international conference on Foundations of augmented cognition
A real-time EEG-based BCI system for attention recognition in ubiquitous environment
Proceedings of 2011 international workshop on Ubiquitous affective awareness and intelligent interaction
EEG: a way to explore learner's affect in pervasive learning systems
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
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The present study was designed to test the efficacy of using Electroencephalogram (EEG) and Event-Related Potentials (ERPs) for making task allocation decisions. Thirty-six participants were randomly assigned to an experimental, yoked, or control group condition. Under the experimental condition, a tracking task was switched between task modes based upon the participant''s EEG. The results showed that the use of adaptive aiding improved performance and lowered subjective workload under negative feedback as predicted. Additionally, participants in the adaptive group had significantly lower RMSE and NASA-TLX ratings than participants in either the yoked or control group conditions. Furthermore, the amplitudes of the N1 and P3 ERP components were significantly larger under the experimental group condition than under either the yoked or control group conditions. These results are discussed in terms of the implications for adaptive automation design.