Fig. 4: Multi-subject model integrated with transfer learning approaches.
From: Transfer learning via distributed brain recordings enables reliable speech decoding
A Schematic of transfer learning for sequence-to-sequence modeling with multiple subjects. Each subject has a unique temporal convolutional layer allowing for subject-specific nonlinear dimensional reduction. Subject-specific features were concatenated, and hidden states were derived across all samples using a shared recurrent layer. The shared encoded features were separated back into subject-specific trials and a linear readout layer was used to decode phonemes sequentially like the above examples. The shared recurrent layer was then frozen and transferred to a subject held-out from the group model. B Multi-subject models show robustness to REO across sensorimotor cortex and temporal lobe. Created in BioRender. Singh, A. (2025) https://BioRender.com/ifs6xbo.
