Recent advancements in brain-computer interfaces (BCIs) have enabled the decoding of lexical tones from intracranial recordings, offering the potential to restore the communication abilities of speech-impaired tonal language speakers. However, data heterogeneity induced by both physiological and instrumental factors poses a significant challenge for unified invasive brain tone decoding. Traditional subject-specific models, which operate under a heterogeneous decoding paradigm, fail to capture generalized neural representations and cannot effectively leverage data across subjects. To address these limitations, we introduce Homogeneity-Heterogeneity Disentangled Learning for neural Representations (H2DiLR), a novel framework that disentangles and learns both the homogeneity and heterogeneity from intracranial recordings across multiple subjects. To evaluate H2DiLR, we collected stereoelectroencephalography (sEEG) data from multiple participants reading Mandarin materials comprising 407 syllables, representing nearly all Mandarin characters. Extensive experiments demonstrate that H2DiLR, as a unified decoding paradigm, significantly outperforms the conventional heterogeneous decoding approach. Furthermore, we empirically confirm that H2DiLR effectively captures both homogeneity and heterogeneity during neural representation learning.

翻译：脑机接口（BCI）的最新进展使得从颅内记录解码词汇声调成为可能，为恢复言语障碍的声调语言使用者的交流能力提供了潜力。然而，由生理和仪器因素引起的数据异质性对统一的侵入式脑声调解码构成了重大挑战。传统的被试特异性模型在异质性解码范式下运行，无法捕捉泛化的神经表征，且不能有效利用跨被试数据。为应对这些局限，我们提出了神经表征的同质性-异质性解耦学习（H2DiLR），这是一个新颖的框架，旨在从多被试的颅内记录中解耦并学习同质性与异质性。为评估H2DiLR，我们收集了多位参与者在朗读包含407个音节（涵盖几乎所有汉字）的普通话材料时的立体脑电图（sEEG）数据。大量实验表明，作为一种统一的解码范式，H2DiLR显著优于传统的异质性解码方法。此外，我们通过实验证实，H2DiLR在神经表征学习过程中有效地捕捉了同质性与异质性。