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Exploring the EEG Correlates of Neurocognitive Lapse with Robust Principal Component Analysis

  • Chun-Shu Wei
  • Yuan-Pin Lin
  • Tzyy-Ping JungEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9743)

Abstract

Recent developments of brain-computer interfaces (BCIs) for driving lapse detection based on electroencephalogram (EEG) have made much progress. This study aims to leverage these new developments and explore the use of robust principal component analysis (RPCA) to extract informative EEG features associated with neurocognitive lapses. Study results showed that the RPCA decomposition could separate lapse-related EEG dynamics from the task-irrelevant spontaneous background activity, leading to more robust neural correlates of neurocognitive lapse as compared to the original EEG signals. This study will shed light on the development of a robust lapse-detection BCI system in real-world environments.

Keywords

EEG BCI RPCA Drowsiness Lapse Driving Fatigue 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Chun-Shu Wei
    • 1
    • 2
    • 3
  • Yuan-Pin Lin
    • 1
  • Tzyy-Ping Jung
    • 1
    • 2
    • 3
    Email author
  1. 1.Swartz Center for Computational Neuroscience, Institute for Neural ComputationAtlantaUSA
  2. 2.Institute of Engineering in MedicineSan DiegoUSA
  3. 3.Department of BioengineeringUniversity of California San Diego, La JollaSan DiegoUSA

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