Device Passport: Enabling Spatio-Temporal Pretrained Models to Generalize Across Input Layouts
For researchers and practitioners using pretrained biosignal models on new devices, this work provides a method to improve cross-layout transfer, though improvements are incremental.
The paper tackles the problem of generalizing biosignal foundation models to new device layouts with different channel configurations. The proposed Device Passport channel embedding technique achieves competitive performance and improves over the strongest learned baseline in layout-transfer regimes.
New device layouts pose a challenging modeling problem due to the lack of large datasets for each specific layout. Biosignal foundation models offer a plausible solution if they are able to generalize to new layouts effectively. To improve cross-layout transfer, we study how different channel embedding techniques behave when pretraining layouts differ substantially from the downstream decoding layout. We propose Device Passport, a new channel embedding technique that learns experts and mixture models that take each channel's functional activity and metadata as input. This contrasts with prior embedding methods, which typically use only functional information or only metadata to look up learned or fixed positional embeddings. Across controlled subset-transfer experiments and realistic transfer to ear-EEG, Device Passport is competitive overall and improves over the strongest learned baseline in the layout-transfer regimes that motivate this work. These results suggest that channel embedding design is a key consideration when reusing large-scale pretrained biosignal models on new devices.