Extracting blinks from continuous eye-tracking data in a mind wandering paradigm

Hollander, John; Huette, Stephanie

doi:10.1016/j.concog.2022.103303

Cited by 12 publications

(5 citation statements)

References 50 publications

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“…Blink behaviour was often used to indicate internal and external attentional focus. Blinking interrupts visual input, temporarily inhibits visual processing, and is reduced when the task requires processing external visual information [ 43 , 44 , 45 ]. Wide eyes are associated with experimental tasks of this type, whereas browsing tasks are external tasks requiring frequent sweeping of one’s surroundings [ 46 , 47 ].…”

Section: Statistical Results and Analysismentioning

confidence: 99%

Study of Ageing in Complex Interface Interaction Tasks: Based on Combined Eye-Movement and HRV Bioinformatic Feedback

Huang

Zhou

Luo

et al. 2022

IJERPH

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Human–computer interaction tends to be intelligent and driven by technological innovation. However, there is a digital divide caused by usage barriers for older users when interacting with complex tasks. To better help elderly users efficiently complete complex interactions, a smart home’s operating system’s interface is used as an example to explore the usage characteristics of elderly users of different genders. This study uses multi-signal physiological acquisition as a criterion. The results of the study showed that: (1) Older users are more attracted to iconic information than textual information. (2) When searching for complex tasks, female users are more likely to browse the whole page before locating the job. (3) Female users are more likely to browse from top to bottom when searching for complex tasks. (4) Female users are more likely to concentrate when performing complex tasks than male users. (5) Males are more likely to be nervous than females when performing complex tasks.

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Section: Statistical Results and Analysismentioning

confidence: 99%

Study of Ageing in Complex Interface Interaction Tasks: Based on Combined Eye-Movement and HRV Bioinformatic Feedback

Huang

Zhou

Luo

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…However, for mind wandering detection, the conventional view on the detrimental impact of ocular artifacts is shifting. Several studies have identified correlations between mind wandering and fixation 44 and blinks 45 , 46 . Consequently, including ocular ‘artifacts’ in the EEG signal may enhance decoding performance 19 .…”

Section: Discussionmentioning

confidence: 99%

EEG complexity measures for detecting mind wandering during video-based learning

Tang,

2024

Sci Rep

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This study explores the efficacy of various EEG complexity measures in detecting mind wandering during video-based learning. Employing a modified probe-caught method, we recorded EEG data from participants engaged in viewing educational videos and subsequently focused on the discrimination between mind wandering (MW) and non-MW states. We systematically investigated various EEG complexity metrics, including metrics that reflect a system’s regularity like multiscale permutation entropy (MPE), and metrics that reflect a system’s dimensionality like detrended fluctuation analysis (DFA). We also compare these features to traditional band power (BP) features. Data augmentation methods and feature selection were applied to optimize detection accuracy. Results show BP features excelled (mean area under the receiver operating characteristic curve (AUC) 0.646) in datasets without eye-movement artifacts, while MPE showed similar performance (mean AUC 0.639) without requiring removal of eye-movement artifacts. Combining all kinds of features improved decoding performance to 0.66 mean AUC. Our findings demonstrate the potential of these complexity metrics in EEG analysis for mind wandering detection, highlighting their practical implications in educational contexts.

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“…Specifically, we excluded every recording during which the participant closed his/her eyes for a duration longer than 10% of the total clip duration to avoid watching, as blinking causes around 5–10% data loss during a recording [ 48 ]. Additionally, we considered faulty recordings those in which participants’ attention was decoupled from the screen stimuli (mind-wandering phenomenon) for a length greater than 20% of the overall duration of the corresponding video clip [ 49 ]. The 20% cutoff was selected based on the evidence provided by a range of studies indicating that mind wandering occurs at least 20% and up to 50% of the time, even during tasks that are not designed to induce it [ 50 , 51 ].…”

Section: Methodsmentioning

confidence: 99%

eSEE-d: Emotional State Estimation Based on Eye-Tracking Dataset

et al. 2023

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Affective state estimation is a research field that has gained increased attention from the research community in the last decade. Two of the main catalysts for this are the advancement in the data analysis using artificial intelligence and the availability of high-quality video. Unfortunately, benchmarks and public datasets are limited, thus making the development of new methodologies and the implementation of comparative studies essential. The current work presents the eSEE-d database, which is a resource to be used for emotional State Estimation based on Eye-tracking data. Eye movements of 48 participants were recorded as they watched 10 emotion-evoking videos, each of them followed by a neutral video. Participants rated four emotions (tenderness, anger, disgust, sadness) on a scale from 0 to 10, which was later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled three self-assessment questionnaires. An extensive analysis of the participants’ answers to the questionnaires’ self-assessment scores as well as their ratings during the experiments is presented. Moreover, eye and gaze features were extracted from the low-level eye-recorded metrics, and their correlations with the participants’ ratings are investigated. Finally, we take on the challenge to classify arousal and valence levels based solely on eye and gaze features, leading to promising results. In particular, the Deep Multilayer Perceptron (DMLP) network we developed achieved an accuracy of 92% in distinguishing positive valence from non-positive and 81% in distinguishing low arousal from medium arousal. The dataset is made publicly available.

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Extracting blinks from continuous eye-tracking data in a mind wandering paradigm

Cited by 12 publications

References 50 publications

Study of Ageing in Complex Interface Interaction Tasks: Based on Combined Eye-Movement and HRV Bioinformatic Feedback

Study of Ageing in Complex Interface Interaction Tasks: Based on Combined Eye-Movement and HRV Bioinformatic Feedback

EEG complexity measures for detecting mind wandering during video-based learning

eSEE-d: Emotional State Estimation Based on Eye-Tracking Dataset

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