CorticAll: A Universal Intracortical Decoder For Communication BCIs

Intracortical brain–computer interfaces (BCIs) for communication remain difficult to scale across participants because their input is heterogeneous: individuals differ in the number and placement of implanted arrays, and each channel samples a distinct neural population. Decoders are therefore typically trained one participant at a time, forgoing the benefits of pooled data. We introduce CorticAll, a multimodal intracortical decoder that accepts heterogeneous channel sets and supports speech, handwriting, and typing decoding alongside cursor control. A shared Channel Tokeniser processes each channel, Channel Identity embeddings preserve its identity, and a cross-attention Channel Mixer maps any number of channels to a fixed-size representation, enabling end-to-end training across datasets. We evaluate CorticAll on 11 datasets from 7 participants, spanning 4 cortical regions and 4 modalities (≈173 h). We further introduce supervised-on-scale, joint supervised training across datasets and modalities, and study it for pooled decoding, fine-tuning, and transfer. Fine-tuning the pooled model reaches state-of-the-art error rates on 7 of the 8 datasets with published baselines and decodes cursor velocity at up to R2 = 0.93. Finally, latent-space analyses reveal substantial shared structure across modalities and participants, suggesting that the model reuses common functional primitives across both.