Subject-Invariant Cross-Modal Decoding of Perceived Speech from Brain Recordings
Perceived speech decoding based on non-invasive brain-computer interface (BCI) signals has been extensively studied in recent years. Research in this field primarily faces two challenges: extracting neural representations with rich spatiotemporal information and achieving cross-subject generalization. Although separate studies have proposed methods to cope with these issues, a unified approach that simultaneously tackles both challenges remains lacking. To fill this gap, we propose the Subject-Invariant Cross-Modal Perceived Speech Decoding (SICMD) method, which integrates functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). We conduct comprehensive analyses of t...
| Authors | Aoke Zhang, Jing Chen |
|---|---|
| Primary category | cs.SD |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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