用拓扑方法分析阅读时的眼动轨迹，提升阅读障碍检测准确率。
Fixation Sequences as Time Series: A Topological Approach to Dyslexia Detection
- 将眼动序列视为时间序列，用拓扑滤波提取多尺度特征。
- 在哥本哈根语料库上，混合模型性能优于仅用传统特征的方法。
- 新提出滤波方法更优，适合有眼动数据的神经科学与教育研究者。
持久同调是拓扑数据分析的一种方法，可从数据中提取鲁棒的多尺度特征。通过在时间序列上施加不同阈值（称为滤波），它能生成稳定的表示。本文提出新型滤波方法，将眼动中的注视序列视为时间序列，构建融合拓扑特征与传统统计特征的混合模型。我们在哥本哈根语料库上评估该方法，该语料库包含母语（L1）与第二语言（L2）阅读障碍者与非阅读障碍者的阅读眼动数据。实验表明，混合模型在阅读障碍检测任务中优于仅依赖传统特征的方法，证明持久同调捕捉到了注视序列中互补的信息。此外，所提拓扑特征性能接近现有基准方法，且新滤波策略优于已有方法。
原文摘要 · Abstract (English)
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.
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