gazeNe t : End-to-end eye-movement event detection with deep neural networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14210%2F19%3A00124621" target="_blank" >RIV/00216224:14210/19:00124621 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.3758/s13428-018-1133-5#citeas" target="_blank" >https://link.springer.com/article/10.3758/s13428-018-1133-5#citeas</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3758/s13428-018-1133-5" target="_blank" >10.3758/s13428-018-1133-5</a>

Result language

angličtina

Original language name

gazeNe t : End-to-end eye-movement event detection with deep neural networks

Original language description

Existing event detection algorithms for eye-movement data almost exclusively rely on thresholding one or more hand-crafted signal features, each computed from the stream of raw gaze data. Moreover, this thresholding is largely left for the end user. Here we present and develop gazeNet, a new framework for creating event detectors that do not require hand-crafted signal features or signal thresholding. It employs an end-to-end deep learning approach, which takes raw eye-tracking data as input and classifies it into fixations, saccades and post-saccadic oscillations. Our method thereby challenges an established tacit assumption that hand-crafted features are necessary in the design of event detection algorithms. The downside of the deep learning approach is that a large amount of training data is required. We therefore first develop a method to augment hand-coded data, so that we can strongly enlarge the data set used for training, minimizing the time spent on manual coding. Using this extended hand-coded data, we train a neural network that produces eye-movement event classification from raw eye-movement data without requiring any predefined feature extraction or post-processing steps. The resulting classification performance is at the level of expert human coders. Moreover, an evaluation of gazeNet on two other datasets showed that gazeNet generalized to data from different eye trackers and consistently outperformed several other event detection algorithms that we tested.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

50103 - Cognitive sciences

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Behavior Research Methods

ISSN

1554-351X

e-ISSN

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Volume of the periodical

51

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

25

Pages from-to

840-864

UT code for WoS article

000465552900025

EID of the result in the Scopus database

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