We just released the correlated components spatial filters from our iScience paper, Attentional state, not trait, predicts test performance in video-based learning.
These filters are mapped onto the standard 64-channel 10–20 EEG montage and are designed for researchers studying neural synchrony during naturalistic learning, video viewing, and shared attention.
Why this matters: in our paper, we found that moment-to-moment attentional state, measured through EEG synchrony while students watched educational videos, predicted test performance. Trait inattention did not explain short-term retention in the same way.
That means attention during learning is measurable, dynamic, and potentially more informative than broad trait labels.
The filters are now available on OSF, so others can apply correlated components analysis without having to rebuild the spatial filters from scratch.
Paper: Click here
Filters: Click here