Next Article in Journal
On the Wireless Microwave Sensing of Bacterial Membrane Potential in Microfluidic-Actuated Platforms
Previous Article in Journal
An Efficient Indoor Positioning Method Based on Wi-Fi RSS Fingerprint and Classification Algorithm
Article

Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device

1
NUS-HCI Lab, Department of Computer Science, School of Computing, National University of Singapore, Singapore 117417, Singapore
2
College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
3
Division of Science, Yale-NUS College, Singapore 138527, Singapore
4
National University of Singapore, Singapore 117417, Singapore
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Filippo Zappasodi
Sensors 2021, 21(10), 3419; https://doi.org/10.3390/s21103419
Received: 31 March 2021 / Revised: 28 April 2021 / Accepted: 8 May 2021 / Published: 14 May 2021
(This article belongs to the Section Wearables)
While numerous studies have explored using various sensing techniques to measure attention states, moment-to-moment attention fluctuation measurement is unavailable. To bridge this gap, we applied a novel paradigm in psychology, the gradual-onset continuous performance task (gradCPT), to collect the ground truth of attention states. GradCPT allows for the precise labeling of attention fluctuation on an 800 ms time scale. We then developed a new technique for measuring continuous attention fluctuation, based on a machine learning approach that uses the spectral properties of EEG signals as the main features. We demonstrated that, even using a consumer grade EEG device, the detection accuracy of moment-to-moment attention fluctuations was 73.49%. Next, we empirically validated our technique in a video learning scenario and found that our technique match with the classification obtained through thought probes, with an average F1 score of 0.77. Our results suggest the effectiveness of using gradCPT as a ground truth labeling method and the feasibility of using consumer-grade EEG devices for continuous attention fluctuation detection. View Full-Text
Keywords: EEG; moment-to-moment; attention detection; wearable; machine learning EEG; moment-to-moment; attention detection; wearable; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Zhang, S.; Yan, Z.; Sapkota, S.; Zhao, S.; Ooi, W.T. Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device. Sensors 2021, 21, 3419. https://doi.org/10.3390/s21103419

AMA Style

Zhang S, Yan Z, Sapkota S, Zhao S, Ooi WT. Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device. Sensors. 2021; 21(10):3419. https://doi.org/10.3390/s21103419

Chicago/Turabian Style

Zhang, Shan, Zihan Yan, Shardul Sapkota, Shengdong Zhao, and Wei T. Ooi. 2021. "Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device" Sensors 21, no. 10: 3419. https://doi.org/10.3390/s21103419

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop