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Distinct Theta Synchrony across Speech Modes: Perceived, Spoken, Whispered, and Imagined

arXiv 2511.07918 ↗First posted 11 Nov 2025cs.CLcited by 0

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Abstract (arXiv)

Human speech production encompasses multiple modes such as perceived, overt, whispered, and imagined, each reflecting distinct neural mechanisms. Among these, theta-band synchrony has been closely associated with language processing, attentional control, and inner speech. However, previous studies have largely focused on a single mode, such as overt speech, and have rarely conducted an integrated comparison of theta synchrony across different speech modes. In this study, we analyzed differences in theta-band neural synchrony across speech modes based on connectivity metrics, focusing on region-wise variations. The results revealed that overt and whispered speech exhibited broader and stronger frontotemporal synchrony, reflecting active motor-phonological coupling during overt articulation, whereas perceived speech showed dominant posterior and temporal synchrony patterns, consistent with auditory perception and comprehension processes. In contrast, imagined speech demonstrated a more spatially confined but internally coherent synchronization pattern, primarily involving frontal and supplementary motor regions. These findings indicate that the extent and spatial distribution of theta synchrony differ substantially across modes, with overt articulation engaging widespread cortical interactions, whispered speech showing intermediate engagement, and perception relying predominantly on temporoparietal networks. Therefore, this study aims to elucidate the differences in theta-band neural synchrony across various speech modes, thereby uncovering both the shared and distinct neural dynamics underlying language perception and imagined speech.

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References · 4

  1. 2021LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interfacecited by 36
  2. 2023Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEGcited by 17
  3. 2022EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classificationcited by 11
  4. 2019SPEAK YOUR MIND! Towards Imagined Speech Recognition With Hierarchical Deep Learningcited by 5

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