Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).

A machine learning model can effectively predict a patient’s risk for a sleep disorder using demographic and lifestyle data, physical exam results and laboratory values, according to a new study ...

Brain–machine interfaces (BMIs) represent a transformative field at the intersection of neuroscience, engineering and computer science, allowing for direct communication between the brain and external ...

First, the data was independently segmented into quintiles (5 levels) for self-relevance and valence based on participant’s ratings. Next, time points (TRs) were assigned according to the levels of ...

In a recent study published in Nature Biomedical Engineering, researchers proposed the utilization of a vision transformer model to decode surgeon activities from surgical videos. The primary ...