Fixation Sequences as Time Series: A Topological Approach to Dyslexia Detection
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 sca...
| Authors | Marius Huber, David R. Reich, Lena A. Jäger |
|---|---|
| Primary category | cs.CL |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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