Pretraining for Sample-Efficient Neural Interfaces
This work proposes MAPA, an otherwise vanilla masked autoencoder with two spatial encodings, an anatomical region embedding and a relative positional encoding that together enable it to learn neural representations that transfer to unseen subjects and across various tasks.
Ben-Ting Tang, Z. Spalding, G. Cogan
· 0 citations