ETDD70: Eye-Tracking Dyslexia Dataset

The ETDD70 dataset comprises eye-tracking recordings from 70 Czech participants, equally divided into dyslexic and non-dyslexic readers, all aged 9–10 years. The dataset captures eye movements during three text-reading tasks in Czech: syllable reading (Task 1), meaningful text reading (Task 4), and pseudo-text reading (Task 5).

This dataset is the result of the project “Diagnostics of Dyslexia Using Eye-Tracking and Artificial Intelligence” conducted by our research team. The project aims to leverage artificial intelligence tools and advanced technical equipment (eye tracking) to more effectively diagnose dyslexia, one of the most common specific learning disorders, and thereby significantly improve re-education strategies for dyslexic students. The primary goal is to develop models that accurately distinguish between dyslexic and non-dyslexic readers based on eye movement patterns recorded during these tasks.

Data collection took place between October 2022 and August 2023, adhering to ethical standards. The project was approved by the Research Ethics Committee of Masaryk University in Brno, Czech Republic.

Please contact us if you have any questions or feedback at [e-mail] or at [e-mail].

The ETDD70 dataset is freely available for research purposes.

PARTICIPANTS

The eye-tracking data were captured from 70 participants: 35 dyslexic and 35 non-dyslexic readers. In all cases, participants are elementary school pupils aged 9-10 years (i.e., 4th grade of elementary school). Recruitment of suitable participants was conducted in cooperation with a psychological counseling center, which facilitated the recruitment of pupils diagnosed with dyslexia. The non-dyslexic readers, who showed no symptoms of dyslexia, were recruited in cooperation with the counseling facilities of selected elementary schools. The dataset was collected from October 2022 to August 2023. The legal representatives of all participants were properly informed about the research procedure and agreed to participate in the study, for which they subsequently received compensation.

STIMULI

We designed three verbal tasks based on standardized paper-based dyslexia diagnostics used in the Czech Republic. These source texts were transferred to a digital version in a controlled form (e.g., amount of text, font size, line spacing, background color, etc.) for the requirements of eye-tracking measurements.

Task called Syllables contains 90 syllables arranged in a 9 x 10 matrix. The syllables are commonly encountered in the Czech language. The individual rows of syllables were categorized according to syllable composition (based on linguistic aspects) as follows: open syllables with no meaning, i.e., consonant + vowel (e.g., "ta," "na"), closed syllables with a central vowel bearing a meaning, i.e., consonant + vowel + consonant (e.g., "suk," "mák"), meaningless syllables consisting of two consonants (e.g., "vl," "bz"), a meaningless syllable formed by a cluster of two consonants ending in a vowel (e.g., "tle," "mra"), and finally a meaningful syllable formed by a cluster of three consonants with one vowel in the 3rd position (e.g., "mrak," "vlak"). All syllables were presented in black font, with Times New Roman on a gray background. The objective of the task is to read aloud all syllables from left to right and from top to bottom. A fixation cross was placed in the lower right corner for gaze-contingent task closure—when the participant looks at this cross, the recording is automatically terminated.

Task called MeaningfulText consists of a passage about a young boy who watches a squirrel from his window. This text is intended for elementary school readers in grades 3 and 4. The stimulus text contains a total of seven text lines with six logical sentences. The text is again written in black-colored font with double line spacing on a grey background and the fixation cross in the lower right corner. The aim of the task is to read the entire text aloud.

Task called PseudoText comprises fictional, meaningless words. This text has a total of seven lines with 15 artificial sentences. The text formatting, as well as the ending fixation cross, are the same as in Task  MeaningfulText. The objective of the task is to read the entire text aloud as smoothly as possible.

EYE-TRACKING FEATURES

The raw eye-tracking data recorded for each task were further processed to extract event-based characteristics—fixations, saccades, and dozens of derived statistical characteristics. The fixations were detected using the i2mc algorithm (Hessels et al., 2017), as it was specifically designed to be noise-robust for measurements in children. The minimum fixation duration was set to 40 ms. The derived characteristics provide additional information about how participants interact with text. These characteristics are divided into whole-task and region-of-interest (ROI) characteristics. While the whole-task characteristics describe the semantics on the global level of the whole screen, the ROI ones characterize the semantics on the local level of a small rectangular area.

Feature-based characteristics for each task:

Syllables

First fixation duration, average fixation duration, number of fixations, number of fixations and saccades without the incoming/outgoing saccade, number of revisits—incoming saccades hitting this ROI from outside.

MeaningfulText, PseudoText

Whole-task (features extracted from the whole trial): number of regressions, ratio of progressive to regressive saccades, average saccadic amplitude, total reading duration, average fixation duration, number of fixations.

ROI (features extracted for separated regions of interest, i.e. lines and words): average fixation duration, number of fixations, number of revisits—incoming saccades hitting this ROI from outside, landing position of the first fixation.

AI CLASSIFICATION APPROACH

The AI-based methods used for the classification of dyslexia are available here: https://gitlab.fi.muni.cz/xsedmid/dyslex

CITE THIS DATASET

Dostalova, N., Svaricek, R., Sedmidubsky, J., Culemann, W., Sasinka, C., Zezula, P., & Cenek, J. (2024). ETDD70: Eye-tracking Dyslexia Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13332134

CITE THE ASSOCIATED PAPER

Sedmidubsky, J., Dostalova, N., Svaricek, R., & Culemann, W. (2024). ETDD70: Eye-tracking dataset for classification of dyslexia using AI-based methods. In Proceedings of the 17th International Conference on Similarity Search and Applications (SISAP) (pp. 1-14). Springer.

ETDD70数据集包含70名捷克参与者的眼动追踪（eye-tracking）记录，参与者均为9~10岁的小学生，按阅读障碍（dyslexia）患者与非阅读障碍读者平均分为两组，每组各35人。该数据集记录了参与者完成三项捷克语文本阅读任务过程中的眼动轨迹，分别为音节阅读任务（任务1）、有意义文本阅读任务（任务4）与伪文本阅读任务（任务5）。

本数据集源自本研究团队开展的“基于眼动追踪与人工智能的阅读障碍诊断”项目。本项目旨在借助人工智能工具与先进的眼动追踪技术设备，更高效地诊断阅读障碍——这是最常见的特异性学习障碍之一，进而显著优化阅读障碍学生的再教育策略。本项目的核心目标是基于上述任务中记录的眼动模式，开发可精准区分阅读障碍者与非阅读障碍者的模型。

数据采集工作于2022年10月至2023年8月间开展，严格遵循伦理规范。本项目已通过捷克布尔诺马萨里克大学（Masaryk University）研究伦理委员会的审查批准。

若您有任何疑问或建议，请通过以下邮箱联系我们：[e-mail] 或 [e-mail]。

ETDD70数据集可免费用于科研用途。

## 参与者
本数据集的眼动追踪数据来自70名小学生：35名阅读障碍患者与35名非阅读障碍读者，年龄均为9~10岁，即小学四年级。本研究与一家心理咨询中心合作招募符合要求的参与者，该中心协助招募已确诊的阅读障碍学生。非阅读障碍读者无任何阅读障碍症状，本研究通过合作的多所小学心理咨询部门完成其招募。数据采集周期为2022年10月至2023年8月。所有参与者的法定监护人事先已充分知晓研究流程并同意参与，随后可获得相应的参与报酬。

## 刺激材料
本研究基于捷克国内通用的纸质阅读障碍诊断标准，设计了三项语言任务。为适配眼动追踪测量需求，将原始文本转换为格式可控的数字版本（如文本总量、字体大小、行间距、背景颜色等）。

### 音节任务（任务1）
包含90个音节，以9×10的矩阵形式呈现。这些音节均为捷克语中常见的音节。根据音节构成的语言学特征，各行音节可分为以下五类：无意义开音节（即辅音+元音结构，如“ta”、“na”）、带意义的闭音节（即辅音+元音+辅音结构，中心元音承载语义，如“suk”、“mák”）、由两个辅音组成的无意义音节（如“vl”、“bz”）、以元音结尾的双辅音簇构成的无意义音节（如“tle”、“mra”），以及由三个辅音簇构成且第三个位置带有一个元音的有意义音节（如“mrak”、“vlak”）。所有音节以黑色Times New Roman字体显示在灰色背景上。本任务要求参与者按照从左到右、从上到下的顺序朗读所有音节。屏幕右下角设置了注视十字（fixation cross），采用视线触发式任务终止机制：当参与者注视该十字时，记录将自动停止。

### 有意义文本任务（任务4）
为一段关于小男孩从窗户观察松鼠的短文，适配小学三、四年级学生的阅读水平。该刺激文本共7行，包含6个逻辑完整的句子。文本同样以黑色字体显示，行间距为双倍，背景为灰色，屏幕右下角同样设有注视十字。本任务要求参与者完整朗读全文。

### 伪文本任务（任务5）
由虚构的无意义词汇组成，文本共7行，包含15个人工构造的句子。文本格式与结束时的注视十字均与有意义文本任务保持一致。本任务要求参与者尽可能流畅地朗读全文。

## 眼动追踪特征
本研究对每项任务记录的原始眼动数据进行进一步处理，提取基于事件的特征——包括注视（fixation）、眼跳（saccade）以及数十种衍生统计特征。注视点检测采用i2mc算法（Hessels等，2017），该算法专为儿童眼动测量的抗噪需求设计。最小注视时长阈值设置为40毫秒。衍生特征可提供参与者与文本交互的额外信息，分为全任务特征与感兴趣区域（region-of-interest, ROI）特征两类：全任务特征从全局层面描述整个屏幕的交互语义，ROI特征则针对小型矩形区域的局部交互语义进行刻画。

各项任务的特征如下：
1. 音节任务
首次注视时长、平均注视时长、注视次数、无入/出眼跳的注视与眼跳次数、回访次数——即从外部进入该ROI的入眼跳次数。
2. 有意义文本任务、伪文本任务
- 全任务特征（从整个试次中提取）：回视次数、前进眼跳与回视眼跳的比率、平均眼跳幅度、总阅读时长、平均注视时长、注视次数。
- ROI特征（从分离的感兴趣区域，即行与单词中提取）：平均注视时长、注视次数、回访次数——即从外部进入该ROI的入眼跳次数、首次注视的着陆位置。

## 人工智能分类方法
本研究用于阅读障碍分类的人工智能方法可通过以下链接获取：https://gitlab.fi.muni.cz/xsedmid/dyslex

## 数据集引用格式
Dostalova, N., Svaricek, R., Sedmidubsky, J., Culemann, W., Sasinka, C., Zezula, P., & Cenek, J. (2024). ETDD70: 眼动追踪阅读障碍数据集 [数据集]. Zenodo. https://doi.org/10.5281/zenodo.13332134

## 关联论文引用格式
Sedmidubsky, J., Dostalova, N., Svaricek, R., & Culemann, W. (2024). ETDD70: 基于人工智能方法的阅读障碍分类眼动追踪数据集. 见第17届相似性搜索与应用国际会议（SISAP 2024）会议论文集（第1-14页）. Springer.