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Advances in The Human Side of Service Engineering pp 115–126Cite as

Classification of EEG Features for Prediction of Working Memory Load

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 494))

Abstract

The objective of this research was to compare classification methods aimed at predicting working memory (WM) load. Electroencephalogram (EEG) data was collected from physicians while performing basic WM tasks and simulated medical scenarios. Data processing was performed to remove noise from the signal used for analysis (e.g., muscle activity, eye-blinks). The data from basic WM tasks was used to develop and test the four classification models (LASSO regression, support vector machines (SVM), nearest shrunken centroids (NSC), and iterated supervised principal components (ISPC) to predict a WM state indicative of physicians’ optimal performance. The naïve misclassification rate was 19.74 %; LASSO and SVM outperformed this threshold: 18.10 and 12.21 % respectively). Both classification models had relatively high-specificity (LASSO: 97.2 %; SVM: 99.8 %); but relatively low-sensitivity LASSO: 20.7 %; SVM: 39.6 %). Results from simulated medical scenarios suggest that physicians were approximately 83 % of the time in the WM state that is likely indicative of optimal performance.

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Acknowledgments

This study was originally funded by the Innovation Center at University of North Carolina (UNC), and the UNC Healthcare System. The data analysis was partially supported by the grant numbers R18HS023458 and R21HS024062 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. Finally, we want to express our gratitude to all participants for their time and effort while participating in our experiments.

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Authors and Affiliations

  1. Department of Statistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, 27599, USA

    Anthony Abrantes

  2. Department of Radiation Oncology, University of North Carolina-Chapel Hill, Chapel Hill, NC, 27599, USA

    Elizabeth Comitz, Prithima Mosaly & Lukasz Mazur

Authors
  1. Anthony Abrantes
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  2. Elizabeth Comitz
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  3. Prithima Mosaly
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  4. Lukasz Mazur
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Corresponding author

Correspondence to Anthony Abrantes .

Editor information

Editors and Affiliations

  1. University of Central Florida , Orlando, Florida, USA

    Tareq Z. Ahram

  2. University of Central Florida , Orlando, USA

    Waldemar Karwowski

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Abrantes, A., Comitz, E., Mosaly, P., Mazur, L. (2017). Classification of EEG Features for Prediction of Working Memory Load. In: Ahram, T., Karwowski, W. (eds) Advances in The Human Side of Service Engineering. Advances in Intelligent Systems and Computing, vol 494. Springer, Cham. https://doi.org/10.1007/978-3-319-41947-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-41947-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41946-6

  • Online ISBN: 978-3-319-41947-3

  • eBook Packages: EngineeringEngineering (R0)

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