Eye-Tracking Dataset to Support the Research on Autism Spectrum Disorder

Cilia, Federica; Carette, Romuald; Elbattah, Mahmoud; Guérin, Jean‐Luc; Dequen, Gilles

doi:10.21203/rs.3.rs-2099817/v1

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…This study adopted a publicly accessible database (Cilia et al, 2022), which involved the eye‐tracking data collected during RJA from 29 autistic children (7.74 ± 2.73) and 30 TD children (8.02 ± 2.89), as well as the clinical data of the CARS evaluation for those children with ASD. An exemption of informed consent was granted because the data were anonymous.…”

Section: Methodsmentioning

confidence: 99%

“…Eye movements during watching videos and photographs were recorded using an SMI RED250 eye tracker (60 Hz; SensoMotoric Instruments, German). All eye‐tracking data can be publicly accessible at the website https://doi.org/10.6084/m9.figshare.20113592 (Cilia et al, 2019; Cilia et al, 2021; Cilia et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

“…Finally, we explored the possibility of using these nonlinear features to predict the severity of ASD symptoms. In order to illustrate our techniques, this study used a publicly accessible database (Cilia et al, 2022) that included the eye‐tracking data collected during RJA from 19 children with ASD (7.82 ± 2.61) and 30 TD children (8.02 ± 2.89), as well as the clinical data of the Childhood Autism Rating Scale (CARS) evaluation (Pilowsky et al, 1998; Rellini et al, 2004) for those children with ASD.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear features of gaze behavior during joint attention in children with autism spectrum disorder

Wang,

Zhao,

2023

Autism Research

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Since children with autism spectrum disorder (ASD) might exhibit a variety of aberrant response to joint attention (RJA) behaviors, there is growing interest in identifying robust, reliable and valid eye‐tracking metrics for determining differences in RJA behaviors between typically developing (TD) children and those with ASD. Previous eye‐tracking studies have not been deeply investigated nonlinear features of gaze time‐series during RJA. As a main motivation, this study aimed to extract three nonlinear features (i.e., complexity, long‐range correlation, and local instability) of gaze time‐series during RJA in children with ASD, which can be measured by fractal dimension (FD), Hurst exponent (H), and largest Lyapunov exponent (LLE), respectively. To illustrate our idea, this study adopted a publicly accessible database, including eye‐tracking data collected during RJA from 19 children with ASD (7.74 ± 2.73) and 30 TD children (8.02 ± 2.89), and conducted a battery of nonparametric analysis of covariance (ANCOVA), where gender was used as covariable. Findings showed that gaze time‐series during RJA in autistic children may generally have greater FD but lower H than that in TD controls. This implies that children with ASD possess more complex and unpredictable gaze behaviors during RJA than TD children. Furthermore, nonlinear metrics outperformed traditional eye‐tracking metrics in obtaining higher identification performance with an accuracy of 82% and an AUC value of 0.81, distinguishing the differences between successful and failed RJA trails, and predicting the severity of ASD symptoms. Findings might bring some new insights into the understanding of the impairments in RJA behaviors for children with ASD.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nonlinear features of gaze behavior during joint attention in children with autism spectrum disorder

Wang,

Zhao,

2023

Autism Research

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“…Our data set was collected by the authors in Ref. [15]. It is comprised of data from 59 children with ages ranging from 3 to 12 years old, both male and female.…”

Section: Methodsmentioning

confidence: 99%

Identifying Autism Gaze Patterns in Five-Second Data Records

Lencastre,

Lotfigolian,

Lind

2024

Diagnostics

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One of the most challenging problems when diagnosing autism spectrum disorder (ASD) is the need for long sets of data. Collecting data during such long periods is challenging, particularly when dealing with children. This challenge motivates the investigation of possible classifiers of ASD that do not need such long data sets. In this paper, we use eye-tracking data sets covering only 5 s and introduce one metric able to distinguish between ASD and typically developed (TD) gaze patterns based on such short time-series and compare it with two benchmarks, one using the traditional eye-tracking metrics and one state-of-the-art AI classifier. Although the data can only track possible disorders in visual attention and our approach is not a substitute to medical diagnosis, we find that our newly introduced metric can achieve an accuracy of 93% in classifying eye gaze trajectories from children with ASD surpassing both benchmarks while needing fewer data. The classification accuracy of our method, using a 5 s data series, performs better than the standard metrics in eye-tracking and is at the level of the best AI benchmarks, even when these are trained with longer time series. We also discuss the advantages and limitations of our method in comparison with the state of the art: besides needing a low amount of data, this method is a simple, understandable, and straightforward criterion to apply, which often contrasts with “black box” AI methods.

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