Combined Linear Regression and Quadratic Classification Approach for an EEG-Based Prediction of Driver Performance

  • Gregory Apker
  • Brent Lance
  • Scott Kerick
  • Kaleb McDowell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8027)


Electroencephalography (EEG) has been used to reliably and non-invasively detect fatigue in drivers. In fact, linear relationships between EEG power-spectral estimates and indices of driver performance have been found during simplified driving tasks. Here we sought to predict driver performance using linear regression in a more complex paradigm. Driver performance varied widely between participants, often varying greatly within a single driving session. We found that a non-selective linear regression model did not generalize well between periods of stable and erratic driving, yielding large errors. However, prediction errors were significantly reduced by training a linear regression model on stable driving for each participant. To provide a confidence estimate for the stable driving model, a quadratic discriminate classifier was trained to detect the transition from stable to erratic driving from the EEG power-spectra. Combined, the regression model and classifier yielded significantly lower prediction errors and provided improved discrimination of poor driving.


EEG Regression Driving Fatigue Power Spectral Density 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gregory Apker
    • 1
  • Brent Lance
    • 1
  • Scott Kerick
    • 1
  • Kaleb McDowell
    • 1
  1. 1.Human Research and Engineering DirectorateU.S. Army Research LaboratoryUSA

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