Towards Homogeneous Lexical Tone Decoding from Heterogeneous Intracranial Recordings
This work addresses the problem of restoring communication for speech-impaired tonal language speakers by improving brain-computer interfaces, though it appears incremental as it builds on existing decoding methods.
The paper tackled the challenge of decoding lexical tones from heterogeneous intracranial recordings across multiple subjects, introducing H2DiLR to disentangle and learn homogeneity and heterogeneity, which significantly outperformed traditional subject-specific models.
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.