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

Monica Betta
Principal InvestigatorPostDoctoral Fellow, PhDScholar, ResearchGate, Twitter
Giacomo Handjaras
Principal InvestigatorResearch Fellow, PhDScholar, ResearchGate, Twitter
Luca Cecchetti
Assistant Professor, PhDScholar, ResearchGate, Twitter
Gabriele Valvano
PhD Student
Adrian Onicaș
PhD Student

What We Publish

Sudden drops in pulse wave amplitude (PWA) measured by finger photoplethysmography (PPG) are known to reflect peripheral vasoconstriction resulting from sympathetic activation. Previous work demonstrated that sympathetic activations during sleep typically accompany the occurrence of pathological respiratory and motor events, and their alteration may be associated with the arising of metabolic and cardiovascular diseases. Importantly, PWA-drops often occur in the absence of visually identifiable cortical micro-arousals and may thus represent a more accurate marker of sleep disruption/fragmentation. In this light, an objective and reproducible quantification and characterization of sleep-related PWA-drops may offer a valuable, non-invasive approach for the diagnostic and prognostic evaluation of patients with sleep disorders. However, the manual identification of PWA-drops represents a time-consuming practice potentially associated with high intra/inter-scorer variability. Since validated algorithms are not readily available for research and clinical purposes, here we present a novel automated approach to detect and characterize significant drops in the PWA-signal. The algorithm was tested against expert human scorers who visually inspected corresponding PPG-recordings. Results demonstrated that the algorithm reliably detects PWA-drops and is able to characterize them in terms of parameters with a potential physiological and clinical relevance, including timing, amplitude, duration and slopes. The method is completely user-independent, processes all-night PSG-data, automatically dealing with potential artefacts, sensor loss/displacements, and stage-dependent variability in PWA-time-series. Such characteristics make this method a valuable candidate for the comparative investigation of large clinical datasets, to gain a better insight into the reciprocal links between sympathetic activity, sleep-related alterations, and metabolic and cardiovascular diseases.

Formant Space Reconstruction From Brain Activity in Frontal and Temporal Regions Coding for Heard Vowels

Rampinini, Handjaras, Leo, Cecchetti, Betta, Marotta, Ricciardi, PietriniFrontiers in Human Neuroscience, 2019. DOI: 10.3389/fnhum.2019.00032
Classical studies have isolated a distributed network of temporal and frontal areas engaged in the neural representation of speech perception and production. With modern literature arguing against unique roles for these cortical regions, different theories have favored either neural code-sharing or cortical space-sharing, thus trying to explain the intertwined spatial and functional organization of motor and acoustic components across the fronto-temporal cortical network. In this context, the focus of attention has recently shifted towards specific model fitting, aimed at motor and/or acoustic space reconstruction in brain activity within the language network. Here, we tested a model based on acoustic properties (formants), and one based on motor properties (articulation), where model-free decoding of evoked fMRI activity during perception, imagery and production of vowels had been successful. Results revealed that phonological information organizes around formant structure during perception of vowels; interestingly, such model was reconstructed in a broad temporal region, outside the primary auditory cortex, but also in the pars triangularis of the left inferior frontal gyrus. Conversely, articulatory features were not associated to brain activity in these regions. Overall, our results call for a degree of interdependence based on acoustic information, between the frontal and temporal ends of the language network.

Modality-independent encoding of individual concepts in the left parietal cortex

Handjaras, Leo, Cecchetti, Papale, Lenci, Marotta, Pietrini, RicciardiNeuropsychologia, 2017. DOI: 10.1016/j.neuropsychologia.2017.05.001
The organization of semantic information in the brain has been mainly explored through category-based models, on the assumption that categories broadly reflect the organization of conceptual knowledge. However, the analysis of concepts as individual entities, rather than as items belonging to distinct superordinate categories, may represent a significant advancement in the comprehension of how conceptual knowledge is encoded in the human brain.Here, we studied the individual representation of thirty concrete nouns from six different categories, across different sensory modalities (i.e., auditory and visual) and groups (i.e., sighted and congenitally blind individuals) in a core hub of the semantic network, the left angular gyrus, and in its neighboring regions within the lateral parietal cortex. Four models based on either perceptual or semantic features at different levels of complexity (i.e., low- or high-level) were used to predict fMRI brain activity using representational similarity encoding analysis. When controlling for the superordinate component, high-level models based on semantic and shape information led to significant encoding accuracies in the intraparietal sulcus only. This region is involved in feature binding and combination of concepts across multiple sensory modalities, suggesting its role in high-level representation of conceptual knowledge. Moreover, when the information regarding superordinate categories is retained, a large extent of parietal cortex is engaged. This result indicates the need to control for the coarse-level categorial organization when performing studies on higher-level processes related to the retrieval of semantic information.

What We Develop

Detection and classification of Eye Movements, and removal of ocular artifacts from EEG

fMRI encoding/decoding algorithms

Our Talks

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.