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Acute Stress Detection Using Recurrence Quantification Analysis of Electroencephalogram (EEG) Signals

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Brain Informatics and Health (BIH 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9919))

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Abstract

In the present work we intend to classify the brain states under physical stress and experimental control conditions based on the nonlinear features of electroencephalogram (EEG) dynamics using support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO). Recurrence Quantification Analysis (RQA) method was employed to quantify the nonlinear features of high-density electroencephalogram (EEG) signals recorded either during instances of acute stress induction or comparison conditions. Four RQA measures, including determinism (DET), entropy (ENTR), laminarity (LAM) and trapping time (TT) were extracted from the EEG signals to characterize the deterministic features of cortical activity. Results revealed that LASSO was highly efficient in classifying the conditions using any one of the selected RQA measures, while SVM achieved accurate classification based solely on ENTR and TT. Among all four measures of non-linear dynamics, ENTR yielded the best overall classification accuracy.

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Acknowledgment

This research is made possible through the funding from the National Science Foundation (grant number CMMI - 1538059), and the Transdisciplinary Area of Excellence (TAE) exploratory research grant provided by State University of New York, Binghamton.

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Authors and Affiliations

  1. State University of New York, Binghamton, NY, USA

    Mohammad Samie Tootooni, Rajesh Sharma Sivasubramony & Vladimir Miskovic

  2. Mechanical and Materials Engineering Department, University of Nebraska-Lincoln, Lincoln, NE, USA

    Prahalada K. Rao

  3. Mechanical and Industrial Engineering Department, Northeastern University, Boston, MA, USA

    Miaolin Fan & Chun-An Chou

Authors
  1. Miaolin Fan
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  2. Mohammad Samie Tootooni
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  3. Rajesh Sharma Sivasubramony
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  4. Vladimir Miskovic
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  5. Prahalada K. Rao
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  6. Chun-An Chou
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Corresponding author

Correspondence to Miaolin Fan .

Editor information

Editors and Affiliations

  1. George Mason University, Fairfax, Virginia, USA

    Giorgio A. Ascoli

  2. Department of Modeling, Analysis and Theory, Allen Institute for Brain Science, Seattle, Washington, USA

    Michael Hawrylycz

  3. University of Nebraska Omaha, Omaha, Nebraska, USA

    Hesham Ali

  4. Information Science and Technology, University of Nebraska Omaha, Omaha, Nebraska, USA

    Deepak Khazanchi

  5. College of Information Sciences and Technology, University of Nebraska Omaha, Omaha, Nebraska, USA

    Yong Shi

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Fan, M., Tootooni, M.S., Sivasubramony, R.S., Miskovic, V., Rao, P.K., Chou, CA. (2016). Acute Stress Detection Using Recurrence Quantification Analysis of Electroencephalogram (EEG) Signals. In: Ascoli, G., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds) Brain Informatics and Health. BIH 2016. Lecture Notes in Computer Science(), vol 9919. Springer, Cham. https://doi.org/10.1007/978-3-319-47103-7_25

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  • DOI: https://doi.org/10.1007/978-3-319-47103-7_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47102-0

  • Online ISBN: 978-3-319-47103-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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