Research
Dr. Toppi’s research interests include the development and implementation of new approaches for high resolution EEG signal processing, with a special focus on brain mapping and brain connectivity in healthy and pathological individuals.
Brain mapping
She contributed to the development of the following methodologies: i) adaptation of the current algorithms for the analysis of event-related potentials in healthy subjects to the non-idealities of data from patients with disorders of consciousness (Risetti et al., Front Hum Neurosci, 2013 – Toppi et al., Neurorehab and Neural Repair, 2019) and ii) source localization approaches aiming at increasing the low spatial resolution of EEG technique and thus identifying brain areas acting as sources in the recorded neuroelectrical activity. Such methods have been then applied to healthy subject with the aim to investigate brain activities associated to imagination (Toppi et al., JNE, 2014) to face perception (Vecchiato et al., Comp Math Meth Med, 2014) and to economic decision making (Vecchiato et al., J. Neurosci Meth, 2010, Vecchiato et al., Med Biol Eng Comp, 2011).
Brain connectivity
She focused on the development of methodologies for stationary and time-varying connectivity estimation and their related statistical assessment against chance (Toppi et al., IEEE Trans Biom Eng, 2016, Toppi et al., Comp Mat Met Med, 2012). Such approaches have been used to reconstruct the brain circuits at the basis of resting brain (Petti et al., CIN, 2016) as well as during active cognitive processes (Toppi et al., Front Hum Neurosci, 2018, Toppi et al., Neuroimage, 2016). In social neuroscience field, within Prof. Astolfi’s group, she was pioneer in the analysis of brain to brain connectivity estimated from hyperscanning EEG acquired (simultaneously) from interacting subjects (Ciaramidaro, Toppi, Sci Rep, 2018, Toppi, PlosOne, 2016, Astolfi et al., IEEE Int Sys, 2011, Astolfi et al., Brain Top, 2010).
Moreover, in the context of CONTRAST project, she employed graph theory indices for quantifying brain networks measures and thus extracting indices to be used as outcome measures in cognitive/motor rehabilitation treatments based on Brain Computer Interface after stroke (Pichiorri et a., Ann of Neu, 2015).
