Fine-tuning language encoding models on slow fMRI improves prediction for fast ECoG

ArXi:2605.19224v1 Announce Type: new Neuroscientists have recently turned to intracranial brain recording methods, like electrocorticography (ECoG), for human experiments because of the fine spatial and temporal resolution that they afford. Models trained on this data, however, are fundamentally restricted by the patient populations that can receive the implants necessary for recording. We propose using non-invasive fMRI to bridge the gap in