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 language | English |
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Title of host publication | Augmented Cognition: 16th International Conference, AC 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings |
Editors | Dylan D. SCHMORROW, Cali M. FIDOPIASTIS |
Publisher | Springer, Cham |
Pages | 213-221 |
Number of pages | 9 |
ISBN (Print) | 9783031054563 |
DOIs | |
Publication status | Published - 2022 |
Externally published | Yes |
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