Cited by 424EEG and Brain-Computer InterfacesSleep and Work-Related FatigueEmotion and Mood Recognition

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A multimodal approach to estimating vigilance using EEG and forehead EOG

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Zheng, Wei-Long, Lu, Bao-Liang·2017·Journal of Neural Engineering

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Abstract

Objective . Covert aspects of ongoing user mental states provide key context information for user-aware human computer interactions. In this paper, we focus on the problem of estimating the vigilance of users using EEG and EOG signals. Approach . The PERCLOS index as vigilance annotation is obtained from eye tracking glasses. To improve the feasibility and wearability of vigilance estimation devices for real-world applications, we adopt a novel electrode placement for forehead EOG and extract various eye movement features, which contain the principal information of traditional EOG. We explore the effects of EEG from different brain areas and combine EEG and forehead EOG to leverage their complementary characteristics for vigilance estimation. Considering that the vigilance of users is a dynamic changing process because the intrinsic mental states of users involve temporal evolution, we introduce continuous conditional neural field and continuous conditional random field models to capture dynamic temporal dependency. Main results . We propose a multimodal approach to estimating vigilance by combining EEG and forehead EOG and incorporating the temporal dependency of vigilance into model training. The experimental results demonstrate that modality fusion can improve the performance compared with a single modality, EOG and EEG contain complementary information for vigilance estimation, and the temporal dependency-based models can enhance the performance of vigilance estimation. From the experimental results, we observe that theta and alpha frequency activities are increased, while gamma frequency activities are decreased in drowsy states in contrast to awake states. Significance . The forehead setup allows for the simultaneous collection of EEG and EOG and achieves comparative performance using only four shared electrodes in comparison with the temporal and posterior sites.

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Authors: Zheng, W.-L., & Lu, B.-L.
Affiliations: Shanghai Jiao Tong University (CN)
Corresponding author: Zheng, Wei-Long
Lu, Bao-Liang
ORCID: Zheng, Wei-Long
Lu, Bao-Liang
Venue: Journal of Neural Engineering
Publisher: IOP Publishing
Published: 2017-02-22
Volume: 14
Issue: 2
Pages: 026017
ISSN: 1741-2552 (ISSN-L)
1741-2560
DOI: 10.1088/1741-2552/aa5a98
OpenAlex: W2558193840
Semantic Scholar: babe42b42bffd9029d7fef3d6c97ec0985fa6528
Cited by: 424
References: 77
Citations by year: 2026: 60 · 2025: 66 · 2024: 72 · 2023: 55 · 2022: 46 · 2021: 50 · 2020: 37 · 2019: 21 · 2018: 14 · 2017: 3
FWCI: 11.78
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: publishingsupport.iopscience.iop.org/iop-standard/v1 (vor)
iopscience.iop.org/info/page/text-and-data-mining (tdm)
Locations: Journal of Neural Engineering (publishedVersion)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: Technology Research and Development Program of China Railway Corporation (2016Z003-B)
National Basic Research Program of China (2013CB329401)
National Natural Science Foundation of China (61272248)
Major Basic Research Program of Shanghai Science and Technology Committee (15JC1400103)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and Work-Related Fatigue
Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Keywords: EEG, multimodal fusion, PERCLOS, eye movement features, temporal dependencies, drowsiness detection, gamma oscillations, wearable EEG
MeSH terms: Adult, Algorithms, Arousal, Electroencephalography, Electrooculography, Female, Humans, Male, Pattern Recognition, Automated, Psychomotor Performance, Sensitivity and Specificity, Reproducibility of Results, Brain Waves

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A multimodal approach to estimating vigilance using EEG and forehead EOG

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