Identifying fixations and saccades in eye-tracking protocols
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Eye Tracking Methodology: Theory and Practice
Eye Tracking Methodology: Theory and Practice
Pupil size variation as an indication of affective processing
International Journal of Human-Computer Studies - Application of affective computing in humanComputer interaction
Frequency analysis of task evoked pupillary response and eye-movement
Proceedings of the 2004 symposium on Eye tracking research & applications
Emotion Recognition Based on Physiological Changes in Music Listening
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and Vision Computing
Psycho-physiological measures for assessing cognitive load
Proceedings of the 12th ACM international conference on Ubiquitous computing
Eye activity as a measure of human mental effort in HCI
Proceedings of the 16th international conference on Intelligent user interfaces
Computer Methods and Programs in Biomedicine
MARGA: Multispectral Adaptive Region Growing Algorithm for brain extraction on axial MRI
Computer Methods and Programs in Biomedicine
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Measuring cognitive load changes can contribute to better treatment of patients, can help design effective strategies to reduce medical errors among clinicians and can facilitate user evaluation of health care information systems. This paper proposes an eye-based automatic cognitive load measurement (CLM) system toward realizing these prospects. Three types of eye activity are investigated: pupillary response, blink and eye movement (fixation and saccade). Eye activity features are investigated in the presence of emotion interference, which is a source of undesirable variability, to determine the susceptibility of CLM systems to other factors. Results from an experiment combining arithmetic-based tasks and affective image stimuli demonstrate that arousal effects are dominated by cognitive load during task execution. To minimize the arousal effect on CLM, the choice of segments for eye-based features is examined. We then propose a feature set and classify three levels of cognitive load. The performance of cognitive load level prediction was found to be close to that of a reaction time measure, showing the feasibility of eye activity features for near-real time CLM.