Sustained Attention States Recognition with EEG and Eye-Tracking in the GradCPT

Wei ZHANG, Yifan ZHANG, Qinyu ZHANG, Jie XU*

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

1 Citation (Scopus)

Abstract

The objective of the current study is to explore the feasibility of online recognition of human sustained attention states using electroencephalography (EEG) and eye-tracking technology in the gradual onset continuous performance task (gradCPT). Sixteen volunteer participants each completed a 2-min practice session and three 8-min experimental sessions of gradCPT. EEG and eye-tracking data were collected during the experimental sessions. Six machine learning algorithms, including logistic regression (LR), linear discriminant analysis (LDA), support vector machines (SVM), random forest (RF), k-nearest neighbors (kNN), and artificial neural networks (ANN), were tested in their performance in recognizing in-the-zone and out-of-the-zone periods. On the behavioral level, the results were consistent with the previous gradCPT studies. Among the machine learning algorithms, SVM and LR yielded above-average performance, with a classification accuracy of 0.62; SVM was the best performer considering balanced sensitivity and specificity. This study demonstrated that it is feasible to detect human sustained station states using frontal-channels EEG and eye-tracking features with above-chance accuracy.
Original languageEnglish
Title of host publicationAugmented Cognition: 16th International Conference, AC 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings
EditorsDylan D. SCHMORROW, Cali M. FIDOPIASTIS
PublisherSpringer, Cham
Pages213-221
Number of pages9
ISBN (Print)9783031054563
DOIs
Publication statusPublished - 2022
Externally publishedYes

Funding

This work was supported by the Aeronautical Science Fund (grant number 20185576005) and the National Natural Science Foundation of China (Grant No. T2192931).

Keywords

  • Electroencephalography
  • Gradual onset continuous performance task
  • Sustain attention

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