Cognitive state prediction using an em algorithm applied to Gamma distributed data

Ali Yousefi; Angelique C. Paulk; Thilo Deckersbach; Darin D. Dougherty; Emad N. Eskandar; Alik S. Widge; Uri T. Eden

doi:10.1109/EMBC.2015.7320205

Cognitive state prediction using an em algorithm applied to Gamma distributed data

Ali Yousefi, Angelique C. Paulk, Thilo Deckersbach, Darin D. Dougherty, Emad N. Eskandar, Alik S. Widge, Uri T. Eden

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Scopus citations

Abstract

Behavioral tests are widely used to quantify features of cognitive processing. For a large class of behavioral signals, the observed variables are non-Gaussian and dynamic; classical estimation algorithms are ill-suited to modeling such data. In this research, we propose a mathematical framework to predict a cognitive state variable related to behavioral signals, which are best modeled using a Gamma distribution. The proposed algorithm combines a Gamma Smoother and EM algorithm in the prediction process. The algorithm is applied to reaction time recorded from subjects performing a Multi-Source Interference Task (MSIT) to dynamically quantify their cognitive flexibility through the course of the experiment.

Original language	English (US)
Title of host publication	2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	7819-7824
Number of pages	6
Volume	2015-November
ISBN (Electronic)	9781424492718
DOIs	https://doi.org/10.1109/EMBC.2015.7320205
State	Published - Nov 4 2015
Externally published	Yes
Event	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy Duration: Aug 25 2015 → Aug 29 2015

Other

Other	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country/Territory	Italy
City	Milan
Period	8/25/15 → 8/29/15

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

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10.1109/EMBC.2015.7320205

Cite this

Yousefi, A., Paulk, A. C., Deckersbach, T., Dougherty, D. D., Eskandar, E. N., Widge, A. S., & Eden, U. T. (2015). Cognitive state prediction using an em algorithm applied to Gamma distributed data. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 (Vol. 2015-November, pp. 7819-7824). [7320205] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2015.7320205

Cognitive state prediction using an em algorithm applied to Gamma distributed data. / Yousefi, Ali; Paulk, Angelique C.; Deckersbach, Thilo et al.
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Vol. 2015-November Institute of Electrical and Electronics Engineers Inc., 2015. p. 7819-7824 7320205.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yousefi, A, Paulk, AC, Deckersbach, T, Dougherty, DD, Eskandar, EN, Widge, AS & Eden, UT 2015, Cognitive state prediction using an em algorithm applied to Gamma distributed data. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. vol. 2015-November, 7320205, Institute of Electrical and Electronics Engineers Inc., pp. 7819-7824, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, Milan, Italy, 8/25/15. https://doi.org/10.1109/EMBC.2015.7320205

Yousefi A, Paulk AC, Deckersbach T, Dougherty DD, Eskandar EN, Widge AS et al. Cognitive state prediction using an em algorithm applied to Gamma distributed data. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Vol. 2015-November. Institute of Electrical and Electronics Engineers Inc. 2015. p. 7819-7824. 7320205 doi: 10.1109/EMBC.2015.7320205

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abstract = "Behavioral tests are widely used to quantify features of cognitive processing. For a large class of behavioral signals, the observed variables are non-Gaussian and dynamic; classical estimation algorithms are ill-suited to modeling such data. In this research, we propose a mathematical framework to predict a cognitive state variable related to behavioral signals, which are best modeled using a Gamma distribution. The proposed algorithm combines a Gamma Smoother and EM algorithm in the prediction process. The algorithm is applied to reaction time recorded from subjects performing a Multi-Source Interference Task (MSIT) to dynamically quantify their cognitive flexibility through the course of the experiment.",

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Cognitive state prediction using an em algorithm applied to Gamma distributed data

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