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EEG-based neurophysiological indicators in pronoun resolution using feature analysis
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DOI：10.1016/j.jneumeth.2025.110462.png)
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Abstract
En 中文
Background: Pronoun resolution is a crucial aspect of language comprehension, yet its underlying neural mechanisms remain poorly understood. While previous studies have explored individual linguistic factors, a systematic analysis of Electroencephalography (EEG)-based neurophysiological indicators across different resolution cues (gender, verb bias, and discourse focus) remains unexplored, limiting our understanding of neuralcognitive processes. New method: We developed an approach combining ReliefF feature selection and Linear Discriminant Analysis (LDA) to analyze EEG data from twenty participants during pronoun resolution tasks. The method examined neural indicators focusing on power spectral density (PSD) and time-domain features, including Zero-Crossing Rate and Peak-to-Peak amplitude. Results: We identified crucial neural indicators across 14 channels and 4 frequency bands, highlighting PSD features in specific channels (AF3, AF4, FC6, F4, T7, T8, and O2) across theta, beta, and gamma bands. Gendercue processing exhibited enhanced neural responses in prefrontal and temporal regions with shorter reaction times (748.77 ms) compared to verb bias (903.20 ms) and discourse focus (948.92 ms). Comparison with existing methods: Unlike previous studies examining individual linguistic factors, our approach simultaneously analyzed multiple resolution cues. The method achieved significant above-chance classification accuracy (49.08 % vs. 33.33 %) across three linguistic factors. This multi-factor analysis provides a more nuanced understanding of pronoun resolution processes than traditional single-factor studies. Conclusions: Our findings suggest a more efficient, feature-driven processing mechanism for gender-cue resolution, contrasting with more complex, reasoning-dependent processing of verb semantics and discourse cues. These insights have implications for developing computational models of language processing and potential clinical applications for language disorders.
Keywords:
EEG
Feature selection
LDA
Neurophysiological classification
Pronoun resolution
Journal
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2.3
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426
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