BCIwiki (bciwiki.com) — A self-supervised pretraining method called MAPA lets an intracranial EEG decoder reach, in a new subject, the accuracy that otherwise requires 3,500 labeled trials using only about 164. The work is by Ben Tang, Zachary Spalding and Gregory B. Cogan and was posted to the arXiv preprint server on September 11, 2026; it has not been peer reviewed.
Brain-computer interfaces decode neural activity to restore lost function, but training a high-performance decoder typically requires collecting a large labeled dataset from every new subject. Self-supervised pretraining is one way to cut that labeling cost: it learns general neural representations from unlabeled recordings accumulated across subjects. For intracranial electroencephalography, however, self-supervised learning has been difficult because contact placement and neuroanatomy differ between subjects.
MAPA adds two spatial encodings to an otherwise vanilla masked autoencoder: an anatomical region embedding and a relative positional encoding. Together, the authors say, these let the model learn neural representations that transfer to unseen subjects and across tasks. MAPA sets a new state of the art across all three regimes of the Neuroprobe benchmark — within-session, cross-session and cross-subject — without fine-tuning.
In the cross-subject regime, a linear probe on MAPA's features needs only about 164 labeled trials to reach the accuracy that takes 3,500 without pretraining, a reduction of more than 90% in labeling demand. The authors conclude that self-supervised pretraining can scale across heterogeneous intracranial EEG recordings and reduce the labeled data needed for accurate decoding in new subjects. The paper runs 15 pages including references and an appendix, with 5 figures; the code is available on GitHub.