Electrodermal activity based study on the relationship between visual attention and eye blink

Sakai, Tsugunosuke; Yoshida, Ryuichi; Tamaki, Haruya; Ogitsu, Takeki; Takemura, Hiroshi; Mizoguchi, Hiroshi; Namatame, Miki; Kusunoki, Fusako; Yamaguchi, Etsuji; Inagaki, Shigenori; Takeda, Yuji; Sugimoto, Masanori; Egusa, Ryohei

doi:10.1109/icsenst.2015.7438468

Cited by 3 publications

(4 citation statements)

References 6 publications

(7 reference statements)

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“…Therefore, by taking the advantage of this similarity between crucial information monitoring and stimulus discrimination, we propose to detect vigilance using ERP signals (P300 and N100). Besides, we utilize the high interdependence of blink rate (a non-ERP feature) with attention capabilities [59] in combination with N100 and P300 ERPs to establish correlation with alertness present in an individual. The motivation behind using blink rate in our study is that a high blink rate indicates low vigilance level of an individual and as, the amplitude of blink is significantly higher comparison to the rhythmic brain activity, this helps in the easy identification of eye blinks.…”

Section: Introductionmentioning

confidence: 99%

Estimation and quantification of vigilance using ERPs and eye blink rate with a fuzzy model-based approach

et al. 2018

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Vigilance or sustained attention is defined as the ability to maintain concentrated attention over prolonged time periods. Many methods for vigilance detection, based on biological and behavioural characteristics, have been proposed in the literature. In general, the existing approaches do not provide any solution to measure vigilance level quantitatively and adopt costly equipment. This paper utilizes a portable EEG device and presents a new method for estimation of vigilance level of an individual by utilizing ERPs (P300 and N100) of EEG signals and eye blink rate. Here, we propose a fuzzy rule-based system using amplitude and time variations of ERPs (N100 and P300) and blink variability to establish the correlation among N100, P300, eye blink and the vigilance activity. Besides, with the help of our proposed fuzzy model we efficiently calculate and quantify the vigilance level, and thereby obtain a numerical value of vigilance instead of its mere presence or absence. To validate the results obtained from our fuzzy model, we performed subjective analysis (to assess the mood and stress level of participants), reaction time analysis and compared the vigilance values with target detection accuracy. The obtained results prove the efficacy of our proposal.

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Section: Introductionmentioning

confidence: 99%

Estimation and quantification of vigilance using ERPs and eye blink rate with a fuzzy model-based approach

et al. 2018

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“…It is important remember that natural cornea temperature is 34. There is a verified relation between attention and blinking rate [16,17]. During periods of increased cognitive load and focus on objects in the environment, the blinking rate decreases [16,18].…”

Section: Simulation Outcomes' Discussionmentioning

confidence: 99%

“…With the extra effects of decreasing blinking rates [16][17][18][19][20], it is quite a fair approximation, since the vapor pressure saturation may be achieved earlier in real world conditions than in simulations.…”

Section: Simulation Outcomes' Discussionmentioning

confidence: 99%

Thermal Effect Estimation of Smartphone Virtual Reality Headsets on Human Eye by Finite Element Method

ULUAYDIN>

ŞEKER

2022

Sakarya University Journal of Science

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Smartphones (SP) terminals are becoming the most popular media for virtual reality (VR) and augmented reality (AR) effects with their central processing unit (CPU) and video capabilities. Simple VR headsets with reasonable costs can host smartphones, and they can together be used for many different applications. But with the outbreak of Covid-19 pandemic, their usage has become essential for many people working from their homes. VR and AR capabilities provide a much richer experience for entertainment, gaming, and video conferencing. The increasing popularity of 3D virtual worlds add up to this usage. On the technology side, multi-radio connectivity is supported both on terminal and network side. A certain risk may arise when using SP VR headsets for such applications requiring a broadband Internet connectivity. SPs with multi-radio connectivity feature may elevate specific absorption rate (SAR) values in those cases. The smartphone used for VR and AR applications is positioned in front of the eyes; and there is very limited ventilation in VR/AR headsets. Authors’ model aims simulate these exposure scenarios in 4G and 5G mobile telecommunication frequencies by finite element method (FEM); and, possible thermal and non-thermal risks of related electromagnetic (EM) radiation on human eye according to the outputs of the model are discussed.

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“…Specifically, our focus is on classifying five different upper facial action units -raising eyebrows (AU01), lowering eyebrows (AU04), raising cheek (AU06), and nose wrinkler (AU09) which are known to be the most important facial actions for pain sensing. In addition to these, we also show that we can detect blinks (AU45) which provides useful information regarding fatigue, attention and even dopaminergic levels [25,46,47,51]. Prior work has focused on detecting these changes using a camera but our goal is to detect these using EOG electrodes on an unobtrusive wearable eyeglass.…”

Section: Challenges In Detecting Upper Facial Actions For Pain Monitomentioning

confidence: 93%

W!nce

Rostaminia

Lamson

Maji

et al. 2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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The ability to unobtrusively and continuously monitor one's facial expressions has implications for a variety of application domains ranging from affective computing to health-care and the entertainment industry. The standard Facial Action Coding System (FACS) along with camera based methods have been shown to provide objective indicators of facial expressions; however, these approaches can also be fairly limited for mobile applications due to privacy concerns and awkward positioning of the camera. To bridge this gap, W!NCE re-purposes a commercially available Electrooculography-based eyeglass (J!NS MEME) for continuously and unobtrusively sensing of upper facial action units with high fidelity. W!NCE detects facial gestures using a two-stage processing pipeline involving motion artifact removal and facial action detection. We validate our system's applicability through extensive evaluation on data from 17 users under stationary and ambulatory settings, a pilot study for continuous pain monitoring and several performance benchmarks. Our results are very encouraging, showing that we can detect five distinct facial action units with a mean F1 score of 0.88 in stationary and 0.82 in ambulatory settings, and that we can accurately detect facial gestures that due to pain. CCS Concepts: • Human-centered computing → Mobile devices; User studies; • Computing methodologies → Neural networks; • Applied computing → Consumer health;

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Electrodermal activity based study on the relationship between visual attention and eye blink

Cited by 3 publications

References 6 publications

Estimation and quantification of vigilance using ERPs and eye blink rate with a fuzzy model-based approach

Estimation and quantification of vigilance using ERPs and eye blink rate with a fuzzy model-based approach

Thermal Effect Estimation of Smartphone Virtual Reality Headsets on Human Eye by Finite Element Method

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