从异质性颅内记录中进行同质词汇声调解码
Towards Homogeneous Lexical Tone Decoding from Heterogeneous Intracranial Recordings
AI总结：
针对多受试者颅内记录的异质性问题，提出 H2DiLR 框架解耦并学习神经表征的同质性与异质性，在407音节普通话 sEEG 数据上实现更优的统一词汇声调解码。
摘要：
脑机接口（BCI）的最新进展已使从颅内记录中解码词汇声调成为可能，从而为恢复声调语言言语障碍者的交流能力提供了潜力。然而，由生理和仪器因素共同引起的数据异质性，对统一的侵入性大脑声调解码构成了重大挑战。传统的受试者特定模型在异质解码范式下运行，无法捕捉泛化的神经表征，也不能有效利用跨受试者的数据。为解决这些局限，我们提出了用于神经表征的同质–异质解耦学习（Homogeneity-Heterogeneity Disentangled Learning for neural Representations，H2DiLR），这是一个新颖框架，可从多名受试者的颅内记录中解耦并学习同质性和异质性。为评估 H2DiLR，我们收集了多名参与者阅读普通话材料时的立体脑电图（sEEG）数据；这些材料包含407个音节，几乎涵盖所有普通话汉字。大量实验表明，H2DiLR 作为一种统一解码范式，显著优于传统的异质解码方法。此外，我们通过实验证实，H2DiLR 在神经表征学习过程中能够有效捕捉同质性和异质性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
