Methods for Advanced Biosignal Analyses
What We Do
Research activities within this topic exploit machine learning, advanced signal processing and mathematical modeling to analyze (f)MRI, M/EEG and peripheral signal recordings in patients and healthy subjects.
Our aim is to accurately model the heterogeneous set of signals of interest and combine information obtained from different imaging techniques to overcome single technique limitations and artefactual confounds. The creation of automated algorithms, developed in strong collaboration with clinicians, provides the opportunity for better comprehend brain-disorders co-morbidities with many cardiovascular and metabolic pathologies and identify effective risk factors, with a diagnostic and prognostic value.
We are involved in the development of multivariate approaches (e.g., MVPA, encoding and decoding algorithms) to analyze neuroimaging data in healthy subjects in collaboration with other research topics. Specifically, we use computational modeling (e.g., visual, acoustic and semantic) to obtain exhaustive stimulus descriptions and to understand the brain mechanisms involved in their encoding.
Disentangling neural activity from non-neural components by combining EEG/fMRI and peripheral biosignals
Development of automated methods for the identification of spatially and/or temporally localized heterogeneities in hd-EEG signals
Model Mediated Inter-Subject Correlation in fMRI
Analyze emotions through Natural Language Processing
Who We Are
What We Publish
What We Develop
Detection and classification of Eye Movements, and removal of ocular artifacts from EEG
fMRI encoding/decoding algorithms
Betta - Hemodynamic cortical and subcortical activity underlying human sleep Slow Waves. Conference of the Italian Society of Psychophysiology and Cognitive Neuroscience, Ferrara, Italy, 2019.
Handjaras - Gradient-based analysis of cortical topography using fMRI. Satellite event of the Annual Conference of the Italian Society of Psychophysiology and Cognitive Neuroscience, Ferrara, Italy, 2019.
Betta - Slow waves of sleep are associated with increased thalamic activity and with a delayed decreased activity in primary sensory cortices. Congress of the Italian Society of Sleep Medicine (AIMS), Genova, Italy, 2019.