Research

The representation of complex systems as networks whose units are functionally connected is ubiquitous in many fields of science, ranging from climatology to econometrics and the study of brain and physiological systems. At BIT lab we develop tools rooted in the general framework of information theory to quantify how information is generated at each node of a complex network, transferred between pairs of nodes, and shared in redundant and synergistic ways among several nodes. Applied to both discrete and continuous time processes studied in the time or frequency domains, we use these tools to describe how network function emerges from network structure and changes across diverse states and conditions.

Main publications:

• L Faes et al., Partial information rate decomposition, Physical review Letters, 2025

• G Mijatovic et al, Network representation of higher-order interactions based on information dynamics, IEEE Transactions on Network Science and Engineering, 2025

• L Sparacino et al, Measuring hierarchically-organized interactions in dynamic networks through spectral entropy rates: theory, estimation, and illustrative application to physiological networks, Neurocomputing, 2025

Based on the view of the human body as an integrated network composed by several organ systems, each with its own internal dynamics but also functionally connected to the other organs, we apply novel methods for multivariate time series analysis to the nonlinear, multi-scale and time-variant output signals of brain, heart and peripheral physiological systems. This integrated unconventional approach aims at providing new insight on the functional structure of the human physiological networks and on its evolution across different physiological states and pathological conditions.

Main publications:

• Y Antonacci et al, A method for the time-frequency analysis of high-order interactions in non-stationary physiological networks, Journal of Neural Engineering, 2025

• L Faes et al, Predictive information decomposition as a tool to quantify emergent dynamical behaviors in physiological networks, IEEE Transactions on Biomedical Engineering, 2025

• L Sparacino et al, 'A method to assess Granger causality, isolation and autonomy in the time and frequency domains: theory and application to cerebrovascular variability', IEEE Transactions on Biomedical Engineering 2024

The brain and the heart are complex systems, whose interaction shows emergent properties that cannot be highlighted by the analysis of each system independently. While clinical evidence points to the functional interplay between brain and body, a solid comprehensive data modelling/processing framework linking the very different physiological dynamics is still missing. We use electroencephalographic (EEG) and electrocardiographic (ECG) data to measure brain and heart activities and we develop and apply data-driven methodologies to identify brain-heart interactions in several different physiological conditions (e.g. enteroception and sleep stages).

Main publications:

• D Candia Rivera et al, Measures and models of brain-heart interaction, IEEE Reviews in Biomedical Engineering, 2026

• VR Vergara et al, Information-theoretic analysis of EEG wave amplitude and heart rate variability reveals the time scale-dependent nature of brain-heart interactions, IEEE Open Journal of Engineering in Medicine and Biology, 2025

• C Barà et al, Local and global measures of information storage for the assessment of heartbeat-evoked cortical responses, Biomedical Signal Processing and Control 2023

Brain connectivity refers to the intricate network of connections that exist within the human brain. It is a fundamental aspect of neuroscience and is crucial for understanding how the brain processes information, performs various functions, and gives rise to cognition and behavior. Among the several definitions of brain connectivity, the concept of functional connectivity is the most known and spread in the literature. It is based on the idea that regions of the brain that are active together, even though anatomically, are likely to be functionally connected. We use functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data to measure brain activity and identify patterns of synchronous neural activity. 

Main publications:

• Y Antonacci et al, Spectral Information Dynamics of Cortical Signals Uncover the Hierarchical Organization of the Human Brain’s Motor Network, IEEE Transactions on Biomedical Engineering, 2025

• Y. Antonacci et al, Measuring Connectivity in Linear Multivariate Processes with Penalized Regression Techniques, IEEE Access 2024

• G Mijatovic et al, An information-theoretic framework to measure the dynamic interaction between neural spike trains, IEEE Transactions on Biomedical Engineering 2021

In recent years, the application of machine learning (ML) algorithms to the analysis of medical data has increased significantly in healthcare research and industry. ML algorithms have been used to help classify physiological and pathological states related to, for example, different types of stress. We develop tools rooted in information theory to support all steps of ML algorithms, i.e., feature extraction, selection and classification, enabling machine learning models to identify hidden correlations and complex patterns. These algorithms provide important support tools for the diagnosis, monitoring, and treatment of physiopathological states, allowing more effective and personalised management of patient health and well-being.

Main publications:

• I Lazic et al, Information-theoretic quantification of high-order feature effects in classification problems, Chaos, Solitons and Fractals, 2026

• C Barà et al, Partial information decomposition for discrete target and continuous source random variables, Physical Review E, 2025

• M Iovino et al, Comparison of automatic and physiologically-based feature selection methods for classifying physiological stress using heart rate and pulse rate variability indices, Physiological Measurement, 2024

This activity focuses on the extraction of relevant information about vital signs, biological rhythms and markers of the physiological state from multiple biomedical signals acquired from portable or minimally invasive wearable devices. The analysis of electrocardiographic, photoplethysmographic and breathing signals is performed to assess the psychophysical state of subjects monitored in different experimental conditions. This approach is employed to assess changes in the vital signs related to altered psychophysiological states, including among others the response to physiological stressors or emotions.

Main Publications:

• G. Volpes et al., Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements, Biosensors, 2024

• S. Valenti et al, Wearable multisensor ring-shaped probe assessing stress and blood oxygenation: design and preliminary measurements, Biosensors, 2023