Brain-computer interfaces (BCIs) can provide a means of communication for individuals with severe neuromuscular diseases, the target end-users. While personalized BCI machine learning models are the current standard, models trained on data from other users could reduce BCI calibration time. We use a novel dataset with BCI users with and without amyotrophic lateral sclerosis (ALS) and a popular BCI deep learning model, EEGNet, to assess the impact of population domain data on transfer learning of a P300 speller task in the ALS cohort. Results show that training on source data from the non-ALS cohort was detrimental to transfer learning. In contrast, generic EEGNet models trained on source data from the ALS cohort performed comparably as user-specific models. Our findings highlight the need for more data from target end-users populations in publicly available BCI datasets.

Recently, the first foundation model developed specifically for image segmentation tasks was developed, termed the "Segment Anything Model" (SAM). SAM can segment objects in input imagery based on cheap input prompts, such as one (or more) points, a bounding box, or a mask. The authors examined the zero-shot image segmentation accuracy of SAM on a large number of vision benchmark tasks and found that SAM usually achieved recognition accuracy similar to, or sometimes exceeding, vision models that had been trained on the target tasks. The impressive generalization of SAM for segmentation has major implications for vision researchers working on natural imagery. In this work, we examine whether SAM’s performance extends to overhead imagery problems and help guide the community’s response to its development. We examine SAM’s performance on a set of diverse and widely studied benchmark tasks. We find that SAM does often generalize well to overhead imagery, although it fails in some cases due to the unique characteristics of overhead imagery and its common target objects. We report on these unique systematic failure cases for remote sensing imagery that may comprise useful future research for the community.