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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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Self-citationThe Vigilance Decrement Reflects Limitations in Effortful Attention, Not Mindlessness
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Bristle-sensors—low-cost flexible passive dry EEG electrodes for neurofeedback and BCI applications
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Combining eye movements and EEG to enhance emotion recognition
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EEG-Based Lapse Detection With High Temporal Resolution
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MethodElectrophysiology-based detection of emergency braking intention in real-world driving
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EEG-based vigilance estimation using extreme learning machines
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Self-citationPeng, J., Bo, L., & Xu, J. (2009) · Cited by 120
Personalizing EEG-based affective models with transfer learning
Zheng, W., & Lu, B. (2016) · International Joint Conference on Artificial Intelligence · Cited by 118
Self-citationLearning a common dictionary for subject-transfer decoding with resting calibration
Morioka, H., Kanemura, A., Hirayama, J.-I., Shikauchi, M., Ogawa, T., Ikeda, S., Kawanabe, M., & Ishii, S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.015 · Cited by 113
Dimensional affect recognition using Continuous Conditional Random Fields
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Driving fatigue detection with fusion of EEG and forehead EOG
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“…existing labelled data from a group of subjects and generalize the models to the new subjects with transfer learning techniques (Pan & Yang 2010, Wronkiewicz, Larson & Lee 2015, Morioka, Kanemura, Hirayama, Shikauchi, Ogawa, Ikeda, Kawanabe & Ishii 2015, Zhang, Zheng & Lu 2015, Zheng & Lu 2016).”
BackgroundMethodSelf-citationOff-line and on-line vigilance estimation based on linear dynamical system and manifold learning
Shi, L.-C., & Lu, B. (2010) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2010.5627125 · Cited by 87
Self-citationZander, T., & Jatzev, S. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/9/1/016003 · Cited by 87 · cited 1×
“Covert aspects of ongoing user mental states provide key context information in user-aware human computer interactions (Zander & Jatzev 2012), which can help systems react adaptively in a proper manner.”
BackgroundTonic Changes in EEG Power Spectra during Simulated Driving
Huang, R., Jung, T., & Makeig, S. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02812-0_47 · Cited by 82
A brain–computer interface method combined with eye tracking for 3D interaction
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Tonic and phasic EEG and behavioral changes induced by arousing feedback
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Method15th Int. Conf on Machine Learning
Cristianini, N., Shawe-Taylor, J. S., & Sybacek, P. (1998) · Cited by 76
PERCLOS: An Alertness Measure of the Past
Trutschel, U. E., Sirois, B., Sommer, D., et al. (2011) · International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design (Driving Assessment Conference) · doi:10.17077/drivingassessment.1394 · Cited by 74
A novel approach to driving fatigue detection using forehead EOG
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“The electrode placement of the forehead EOG (Zhang, Gao, Zhu, Zheng & Lu 2015) is shown in Figure 2.”
MethodSelf-citationEEG predictors of covert vigilant attention
Martel, A., Dähne, S., & Blankertz, B. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/3/035009 · Cited by 65 · cited 3×
“…previous findings in the literature (Ray & Cole 1985, Davidson et al. 2007, Huang et 2009, O’Connell et al. 2009, Peiris et al. 2011, Lin et al. 2013, Martel et al. 2014) and support the previous evidence that the increasing trend for the ratio of slow and fast waves of EEG activities reflects…”
ResultDetection of lapses in responsiveness from the EEG
Peiris, M. T. R., Davidson, P. R., Bones, P. J., & Jones, R. D. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/1/016003 · Cited by 56 · cited 2×
“In these cases, a decrease in vigilance (Grier, Warm, Dember, Matthews, Galinsky, Szalma & Parasuraman 2003) or a momentary lapse of attention (Peiris, Davidson, Bones & Jones 2011, Davidson, Jones & Peiris 2007) might severely endanger public transportation safety.”
BackgroundResultEvaluating driving fatigue detection algorithms using eye tracking glasses
Gao, X., Zhang, Y.-F., Zheng, W., et al. (2015) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2015.7146736 · Cited by 53 · cited 2×
“…existing labelled data from a group of subjects and generalize the models to the new subjects with transfer learning techniques (Pan & Yang 2010, Wronkiewicz, Larson & Lee 2015, Morioka, Kanemura, Hirayama, Shikauchi, Ogawa, Ikeda, Kawanabe & Ishii 2015, Zhang, Zheng & Lu 2015, Zheng & Lu 2016).”
BackgroundMethodSelf-citationFoundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience
Schmorrow, D., & Fidopiastis, C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-39952-2 · Cited by 45
Can arousing feedback rectify lapses in driving? Prediction from EEG power spectra
Lin, C., Huang, K.-C., Chuang, C., Ko, L., & Jung, T. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/5/056024 · Cited by 43 · cited 3×
“By monitoring the changes of EEG patterns, they were able to detect driving performance and estimate the efficacy of arousing warning feedback delivered to drowsy subjects (Lin, Huang, Chuang, Ko & Jung 2013).”
BackgroundResultInfluentialFuzzy Fusion of Eyelid Activity Indicators for Hypovigilance-Related Accident Prediction
Damousis, I. G., & Tzovaras, D. K. (2008) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2008.928241 · Cited by 42 · cited 1×
“…information on various eye movements, which are often utilized to estimate vigilance because of its easy setup and high signal-noise ratio (Papadelis, Chen, Kourtidou-Papadeli, Bamidis, Chouvarda, Bekiaris & Maglaveras 2007, Damousis & Tzovaras 2008, Ma, Shi & Lu 2010, Ma, Shi & Lu 2014).”
BackgroundDelayed verbal reactions are specifically impaired in patients with schizophrenia
Pinelli, P., Ceriani, F., Colombo, R., Pasetti, C., Terazzi, M., & Castignoli, G. (2000) · International Journal of Psychophysiology · doi:10.1016/s0167-8760(00)00088-x · Cited by 38
An EOG-based Vigilance Estimation Method Applied for Driver Fatigue Detection
Ma, J., Shi, L.-C., & Lu, B. (2014) · Neuroscience and Biomedical Engineering · doi:10.2174/2213385202666141218104855 · Cited by 38 · cited 2×
“…information on various eye movements, which are often utilized to estimate vigilance because of its easy setup and high signal-noise ratio (Papadelis, Chen, Kourtidou-Papadeli, Bamidis, Chouvarda, Bekiaris & Maglaveras 2007, Damousis & Tzovaras 2008, Ma, Shi & Lu 2010, Ma, Shi & Lu 2014).”
BackgroundMethodSelf-citationLeveraging anatomical information to improve transfer learning in brain–computer interfaces
Wronkiewicz, M., Larson, E. B., & Lee, A. K. C. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/4/046027 · Cited by 36 · cited 1×
“…existing labelled data from a group of subjects and generalize the models to the new subjects with transfer learning techniques (Pan & Yang 2010, Wronkiewicz, Larson & Lee 2015, Morioka, Kanemura, Hirayama, Shikauchi, Ogawa, Ikeda, Kawanabe & Ishii 2015, Zhang, Zheng & Lu 2015, Zheng & Lu 2016).”
BackgroundTransfer Components Between Subjects for EEG-based Driving Fatigue Detection
Zhang, Y., Zheng, W., & Lu, B. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-26561-2_8 · Cited by 35
Self-citationHuman implicit intent recognition based on the phase synchrony of EEG signals
Kang, J.-S., Park, U., Gonuguntla, V., Veluvolu, K. C., & Lee, M.-H. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.06.013 · Cited by 35 · cited 1×
“Various studies have introduced the assessment of the mental states of users, such as intention, emotion, and workload, to promote active interactions between users and machines (M¨uhl, Jeunet & Lotte 2014, Lu, Zheng, Li & Lu 2015, Kang, Park, Gonuguntla, Veluvolu & Lee 2015, Zheng & Lu 2015).”
BackgroundVigilance estimation by using electrooculographic features
Ma, J., Shi, L.-C., & Lu, B. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5627122 · Cited by 31 · cited 2×
“…information on various eye movements, which are often utilized to estimate vigilance because of its easy setup and high signal-noise ratio (Papadelis, Chen, Kourtidou-Papadeli, Bamidis, Chouvarda, Bekiaris & Maglaveras 2007, Damousis & Tzovaras 2008, Ma, Shi & Lu 2010, Ma, Shi & Lu 2014).”
BackgroundMethodSelf-citationImbrasaitė, V., Baltrušaitis, T., & Robinson, P. (2014) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew.2014.6890697 · Cited by 14 · cited 1×
“For more details regarding the learning and inference of CCRF and CCNF, please refer to (Baltruˇsaitis et al. 2014, Imbrasaite et al. 2014).”
MethodSleeping and driving: Not a safe dual-task
Oken, B., & Salinsky, M. (2007) · Clinical Neurophysiology · doi:10.1016/j.clinph.2007.06.004 · Cited by 11
Research works printed but not linked: 1
- Dinges, 1998, PERCLOS: a valid psychophysiological measure of alertness as assessed by psychomotor vigilance, US Department of Transportation, Federal Highway Administration, Publication Number FHWA-MCRT-98-006
Cited by
Sorted by citations
A review of emotion recognition using physiological signals
Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., & Yang, X. (2018) · Sensors · doi:10.3390/s18072074 · Cited by 923
“The SEED database [135] contains EEG and eye movement of three different emotions (positive, neutral and negative).”
BackgroundEEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation
Gao, Z., Wang, X., Yang, Y., Mu, C., Cai, Q., Dang, W., & Zuo, S. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2886414 · Cited by 486
“In [17], a multimodal approach was proposed to estimate driver fatigue by combin-”
Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016
Wu, D., Xu, Y., & Lu, B.-L. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3007453 · Cited by 434
“There are many important BCI regression problems, e.g., driver drowsiness estimation [38]–[40], vigilance estimation [11], [12], [113], and user reaction time estimation [41], which were not adequately addressed in previous reviews.”
Self-citationTian, L., Zimmerman, B., Akhtar, A., Yu, K. J., Moore, M., Wu, J. C., Larsen, R. J., Lee, J. W., Li, J., Liu, Y., et al. (2019) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0347-x · Cited by 411
EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities
Suhaimi, N. S., Mountstephens, J., & Teo, J. (2020) · Computational Intelligence and Neuroscience · doi:10.1155/2020/8875426 · Cited by 395
“As for video-related contents, SEED [124–126], DREAMER [127], and ASCERTAIN [107] do provide their video database either openly or upon request.”
Background
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
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