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This paper introduces a novel application of persistent homology to analyze eye-tracking data for dyslexia detection, treating fixation sequences as time series and developing new filtration methods tailored for this domain. By constructing "hybrid models" that combine topological features derived from persistent homology with traditional statistical features, the authors achieve superior performance compared to methods relying solely on statistical features. Experiments on the Copenhagen Corpus demonstrate that the topological features capture complementary information, leading to improved dyslexia detection accuracy.
Persistent homology, when applied to eye-tracking data via novel filtration techniques, unlocks dyslexia detection performance exceeding traditional statistical methods.
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.
