The rank-(L, L, 1, 1) block term decomposition (BTD) model shows better separation performance for multi-subject fMRI data by preserving the high-way structure of fMRI data than canonical polyadic decomposition (CPD). However, multi-subject fMRI data are noisy and have high spatiotemporal variability. To address these limitations, this paper proposes a novel 3D weighted spatial pooling preprocessing that compresses and smooths multi-subject fMRI data and assigns a higher weight to in-brain voxels but a lower weight to out-brain voxels. This strategy not only largely reduces the size of spatial...
