Fixation Sequences as Time Series: A Topological Approach to Dyslexia Detection

2026-04-23

AI Summary

This study addresses the automatic identification of dyslexia by proposing a hybrid model that integrates topological data analysis with conventional statistical features. The method models eye-tracking fixation sequences as time series and introduces a novel filtering strategy tailored specifically for fixation data. For the first time in this domain, persistent homology is employed to extract higher-order topological structures from the filtered sequences. Experimental results on the Copenhagen Eye-Tracking Corpus demonstrate that the proposed filtering approach outperforms existing methods, and that incorporating topological features into the hybrid model significantly enhances detection performance. These findings confirm the complementary value of topological information relative to traditional statistical features in dyslexia identification.

Technology Category

• Natural Language Processing: Information Extraction
• Machine Learning: Evaluation and Analysis
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Persistent homology, a method from topological data analysis, extracts robust, multi-scale features from data. It produces stable representations of time series by applying varying thresholds to their values (a process known as a \textit{filtration}). We develop novel filtrations for time series and introduce topological methods for the analysis of eye-tracking data, by interpreting fixation sequences as time series, and constructing ``hybrid models'' that combine topological features with traditional statistical features. We empirically evaluate our method by applying it to the task of dyslexia detection from eye-tracking-while-reading data using the Copenhagen Corpus, which contains scanpaths from dyslexic and non-dyslexic L1 and L2 readers. Our hybrid models outperform existing approaches that rely solely on traditional features, showing that persistent homology captures complementary information encoded in fixation sequences. The strength of these topological features is further underscored by their achieving performance comparable to established baseline methods. Importantly, our proposed filtrations outperform existing ones.

Problem

• dyslexia detection
• eye-tracking
• fixation sequences
• time series
• topological data analysis

Innovation

• persistent homology
• topological data analysis
• fixation sequences
• filtrations
• dyslexia detection