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

TL;DR

This paper introduces a topological data analysis approach using persistent homology to extract features from eye-tracking fixation sequences, improving dyslexia detection accuracy.

Contribution

It develops novel filtrations for time series and combines topological features with traditional statistical features for better dyslexia classification.

Findings

Hybrid models outperform traditional feature-only approaches.

Topological features achieve comparable performance to established baselines.

Proposed filtrations outperform existing topological methods.

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.