Assunta Ciarlo

The paper examines how signal quality affects inter-individual variability in fNIRS data and introduces an open dataset with multi-task recordings and peripheral physiology measures.

This paper introduces a subject-level searchlight optimization framework combining univariate and multivariate analyses to identify condition-sensitive voxels for emotional state decoding in fMRI.

This study explores using real-time representational similarity analysis in fMRI neurofeedback to navigate emotional states, showing feasibility and improved performance with repeated sessions.

This study identifies classification inaccuracies in fMRI-based attention state decoding due to design choices and confounders like motion artifacts and task timing.