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Related Experiment Video

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Unsupervised fNIRS feature extraction with CAE and ESN autoencoder for driver cognitive load classification.

Ruixue Liu1, Bryan Reimer2, Siyang Song3

  • 1Worcester Polytechnic Institute, P.O. Box 1212, Worcester, MA 016091, United States of America.

Journal of Neural Engineering
|December 11, 2020
PubMed
Summary

This study introduces an advanced machine learning framework using functional near-infrared spectroscopy (fNIRS) to objectively measure driver cognitive load. The Echo State Network autoencoder demonstrated superior performance in classifying cognitive load levels for enhanced road safety.

Keywords:
convolutional autoencoderdriver cognitive loadecho state networkfNIRSfunctional near-infrared spectroscopy

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Transportation Safety

Background:

  • Driver cognitive load is critical for road safety.
  • Objective measurement of cognitive load is needed.
  • Functional near-infrared spectroscopy (fNIRS) offers potential for brain sensing.

Purpose of the Study:

  • Develop an advanced machine learning framework for classifying driver cognitive load.
  • Utilize fNIRS data for objective cognitive load assessment.
  • Enhance road safety through better understanding of driver mental states.

Main Methods:

  • A driving simulator study was conducted with participants performing the N-back task.
  • Functional near-infrared spectroscopy (fNIRS) was used to collect brain data.
  • Convolutional Autoencoder (CAE) and Echo State Network (ESN) autoencoder were employed for feature extraction and classification.

Main Results:

  • CAE achieved 73.25% accuracy for two cognitive load levels and 47.21% for four levels.
  • The proposed ESN autoencoder reached 80.61% accuracy for two levels and 52.45% for four levels.
  • ESN autoencoder provided state-of-the-art group-level classification without window selection.

Conclusions:

  • This research establishes a foundation for using fNIRS to measure driver cognitive load in real-world scenarios.
  • The ESN autoencoder effectively extracts temporal information from fNIRS data.
  • The developed framework shows promise for various fNIRS data classification applications.