Independent-Component-Based Encoding Models of Brain Activity During Story Comprehension

arXiv:2604.24942v2 Announce Type: replace Abstract: Encoding models provide a powerful framework for linking continuous stimulus features to neural activity; however, traditional voxelwise approaches are limited by measurement noise, inter-subject variability, and redundancy arising from spatially correlated voxels encoding overlapping neural signals. Here, we propose an independent component (IC)-based encoding framework that dissociates stimulus-driven and noise-driven signals in fMRI data. We decompose continuous fMRI data from naturalistic story listening into ICs using one subset of the d

Source: arXiv cs.CL — read the full report at the original publisher.

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