EEG Correlates of Driving Performance

Thomas G. Simpson, Karen Rafferty

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
163 Downloads (Pure)

Abstract

Techniques for monitoring human performance traditionally rely on subjective responses and task-specific scoring, yet research suggests EEG could offer multiple performance metrics with high temporal resolution and accuracy that could be leveraged for human–computer interaction purposes. The objective of the presented work is to investigate which EEG responses correlate with task performance and evaluate whether combinations of these produce effective predictive models, facilitating further understanding of the psychological link to performance. A user study was conducted with 32 participants required to negotiate a driving course with the ambition of learning and improving ability on the course during an EEG recording session. EEG was filtered and post-processed to find power spectral density in alpha (α), beta (β), delta (δ), and theta (θ) frequency bands, as well as frontal alpha asymmetry (FAA). The initial laps were considered a baseline and an average performance improvement was calculated over the remaining laps in terms of percentage improvement in duration of track traversal. Results demonstrate event related desynchronization with increased task performance in the alpha (p =. 000), delta (p =. 000), and theta (p =. 000) bands, as well as evidence of a relationship between overall change in FAA and task efficiency. A full electrode analysis identifies δF4 as the optimal for predicting collisions, with efficiency best predicted by a combination of βOz and δF4 .
Original languageEnglish
Number of pages16
JournalIEEE Transactions on Human-Machine Systems
Early online date13 Jan 2022
DOIs
Publication statusEarly online date - 13 Jan 2022

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Brain modeling
  • Electroencephalography
  • human performance monitoring
  • Measurement
  • Monitoring
  • Psychology
  • regression modeling
  • Task analysis
  • Training

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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