Combining eye tracking, pupil dilation and EEG analysis for predicting web users click intention

Slanzi, Gino; Balazs, Jorge A.; Velsquez, Juan D.

doi:10.1016/j.inffus.2016.09.003

Cited by 61 publications

(36 citation statements)

References 31 publications

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“…They obtained F1 scores in the range of 0.5 -0.7 using RF and SVM. Slanzi et al [52] predicted web-surfer's click-intention from eye-tracking features. They used a battery of classifiers, but the F1 scores were not promising.…”

Section: Related Work 21 Information Relevance and Eye-trackingmentioning

confidence: 99%

“…We also compared our method to existing approaches for inferring relevance using eye-movements, where the data is collapsed into a set of handcrafted features (discussed in Section 2.1). Perceived-relevance of documents are predicted from these features using popular classifiers like Random Forests [34,64] and Support Vector Machines (SVM) [38,52]. We computed 20 such hand-engineered features, aggregated at the user-doc level, and classified them using Random Forest and SVM.…”

Section: Comparison To Existing Standardmentioning

confidence: 99%

“…Simola et al [50] predicted task-category (word search, question answer, or reading by interest) from the scanpath, and they obtained 59.8% accuracy using logistic regression on fixation count, mean and SD of fixation durations, and mean and SD of saccade length. Slanzi et al [52] predicted the click-intention of web users. Though they initially considered using eye-tracking features, those were later discarded using Random Lasso feature selection, and EEG and pupillometry features were mainly used.…”

Section: Comparison With Related Workmentioning

confidence: 99%

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Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks

Bhattacharya

Rakshit

Gwizdka

et al. 2020

Proceedings of the 2020 Conference on Human Information Interaction and Retrieval

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We propose an image-classification method to predict the perceivedrelevance of text documents from eye-movements. An eye-tracking study was conducted where participants read short news articles, and rated them as relevant or irrelevant for answering a trigger question. We encode participants' eye-movement scanpaths as images, and then train a convolutional neural network classifier using these scanpath images. The trained classifier is used to predict participants' perceived-relevance of news articles from the corresponding scanpath images. This method is content-independent, as the classifier does not require knowledge of the screen-content, or the user's information-task. Even with little data, the image classifier can predict perceived-relevance with up to 80% accuracy. When compared to similar eye-tracking studies from the literature, this scanpath image classification method outperforms previously reported metrics by appreciable margins. We also attempt to interpret how the image classifier differentiates between scanpaths on relevant and irrelevant documents.

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Section: Related Work 21 Information Relevance and Eye-trackingmentioning

confidence: 99%

Section: Comparison To Existing Standardmentioning

confidence: 99%

Section: Comparison With Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks

Bhattacharya

Rakshit

Gwizdka

et al. 2020

Proceedings of the 2020 Conference on Human Information Interaction and Retrieval

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“…A comparable experiment [4] by Slanzi on predicting click intention of web users was conducted with eye tracking and EEG. It suggests the possibility of creating a classifier for the prediction based on the gaze position, pupil dilation and EEG responses.…”

Section: Consumer Behaviormentioning

confidence: 99%

Study on Applications of Eye Tracking in Different Fields

Ke¹,

Bo-gang²,

Yang³

2018

dtcse

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Abstract. The arising technology eye tracking has been blooming not only in the medical field but also in the field of computer science. There are various usages of eye tracking that range from human study, over usability of design, up to controller input of computer interfaces to information security. In this paper, the application of the real-time gaze-base technique eye tracking is introduced with the presented usages. A comprehensive summary shows the progress of this innovative technology. Finally, the challenges and potential directions of this uprising tool are being discussed.

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“…A recent paper [11] aims to predict user click intentions on webpages based on pupil dilation and EEG data. They extracted 15 features from the pupil and EEG data.…”

Section: Introductionmentioning

confidence: 99%