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This work proposes the Subject-Invariant Cross-Modal Perceived Speech Decoding (SICMD) method, which integrates functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) and conducts comprehensive analyses of the fusion method, fusion position, encoder architecture, and model inputs.
A residual sensor adapter unifies input dimensions, intermediate dilated-block states retain temporal depth, and a target- and time-dependent gate fuses local hierarchical and attention-enhanced context predictions in a Sequential Hierarchical Integration Network for EEG and MEG.
End-to-end system operation and support online reuse without test-day calibration are demonstrated and AESSI, an around-ear SSI using masked-context representation pretraining (MCRP), an around-ear SSI using masked-context representation pretraining is presented.
Decoding perceived speech from non-invasive brain recordings has garnered significant attention in recent years due to its wide range of potential applications. However, existing methods face considerable challenges in cross-subject decoding, primarily due to limited generalizability and the absence of explicit mechani...
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