Pretraining Cuts Labeled Data Needed for BCI Decoding by Over 90%
Training a decoder to read brain signals usually means collecting a large labeled dataset from every new subject, which is slow and a burden on patients. The proposed method, MAPA, first runs self-supervised pretraining on unlabeled intracranial EEG recordings pooled across subjects, then transfers to new ones. The difficulty is that electrode contact placement and neuroanatomy vary from person to person, so MAPA adds two spatial encodings, an anatomical region embedding and a relative positional encoding, to a standard masked autoencoder. In cross-subject tests, about 164 labeled trials were enough to reach the accuracy that otherwise takes 3,500. The team reports that MAPA set new best results on the Neuroprobe benchmark in all three settings, within-session, cross-session and cross-subject, without fine-tuning, suggesting that calibration for implanted BCIs could become much shorter.