Causal patching localizes vocalized/mimed/imagined speech transfer to a compact subspace in a real brain-to-speech decoder

Maghsoudi & Mishra (2026) apply cross-mode activation patching, causal scrubbing, and coarse-to-fine causal tracing to a Conv1D-encoder + 3-layer bidirectional-GRU + HiFi-GAN decoder trained on real human sEEG (VOCALMIND dataset, vocalized/mimed/imagined speech) [maghsoudi-mishra-2026-mechanistic-interpretability-of-brain-to-speech-models] Patching vocalized-mode activations into the imagined-mode pathway raises reconstruction PCC from 0.725 to 0.954; the reverse direction collapses PCC to 0.177, establishing sufficiency and necessity [maghsoudi-mishra-2026-mechanistic-interpretability-of-brain-to-speech-models] Causal scrubbing localizes the transfer to a 16-channel convolutional subspace (channels 32-48) and a specific RNN time window (steps 21-84) -- KEEP-Conv (0.666 PCC) beats size-matched RAND-Conv (0.564) -- while tri-modal linear interpolation produces smooth, monotonic transitions with mimed speech landing intermediate, consistent with a shared continuous causal manifold across speech modes [maghsoudi-mishra-2026-mechanistic-interpretability-of-brain-to-speech-models]