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A multimodal approach to estimating vigilance using EEG and forehead EOG
Open accessAbstract
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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“Zheng and his colleges presented an emotion recognition method combining EEG signals and pupillary response collected from eye tracker and achieved average accuracies of 73.59% and 72.98% for three emotion states using feature level fusion strategy and decision-level fusion strategy, respectively.”
From Fusion of Facial Expressions and EEG for Multimodal Emotion Recognition (2017) · cites this paper as [32]
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“Most commercial biometric systems do not provide access to the feature sets nor the raw data which they use in their products [22].”
From Fusion of Facial Expressions and EEG for Multimodal Emotion Recognition (2017) · cites this paper as [22]
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“Superior performance was obtained compared to the results in the state-of-the-art results [3, 11, 32].”
From Fusion of Facial Expressions and EEG for Multimodal Emotion Recognition (2017) · cites this paper as [32]
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Metadata
- Authors
- Zheng, W.-L., & Lu, B.-L.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- 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
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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