Using fNIRS for Real-Time Cognitive Workload Assessment.

Hincks, Samuel W.
Afergan, Daniel.
Jacob, Robert J. K.
2016

In this paper, we evaluate the possibility of detecting continuous changes in the user's cognitive workload using functional near-infrared spectroscopy (fNIRS) We dissect the source of meaning in a large collection of n-backs and argue that the problem of controlling the content of a participant's mind poses a major problem for calibrating an algorithm using black box machine learning. We therefor... read more

Subjects
Machine learning.
Computer science.
Cognition.
Tufts University. Department of Computer Science.
Permanent URL
http://hdl.handle.net/10427/009812
Original publication
S. W. Hincks, D. Afergan, and R. J. K. Jacob, "Using fNIRS for Real-Time Cognitive Workload Assessment," in Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience: 10th International Conference, AC 2016, Held as Part of HCI International 2016, Toronto, ON, Canada, July 17-22, 2016, Proceedings, Part I, D. D. Schmorrow and C. M. Fidopiastis, Eds., ed Cham: Springer International Publishing, 2016, pp. 198-208. The final publication is available at http://link.springer.com/chapter/10.1007%2F978-3-319-39955-3_19.
ID: tufts:18448
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