Alberto Sorrentino
Citata da
Citata da
Dynamical MEG source modeling with multi‐target Bayesian filtering
A Sorrentino, L Parkkonen, A Pascarella, C Campi, M Piana
Human brain mapping 30 (6), 1911-1921, 2009
Simultaneous human intracerebral stimulation and HD-EEG, ground-truth for source localization methods
E Mikulan, S Russo, S Parmigiani, S Sarasso, FM Zauli, A Rubino, ...
Scientific data 7 (1), 127, 2020
Forward simulation and inverse dipole localization with the lowest order Raviart—Thomas elements for electroencephalography
S Pursiainen, A Sorrentino, C Campi, M Piana
Inverse Problems 27 (4), 045003, 2011
A Rao–Blackwellized particle filter for magnetoencephalography
C Campi, A Pascarella, A Sorrentino, M Piana
Inverse Problems 24 (2), 025023, 2008
Sequential Monte Carlo samplers for semi-linear inverse problems and application to magnetoencephalography
S Sommariva, A Sorrentino
Inverse Problems 30 (11), 114020, 2014
Bayesian multi-dipole modelling of a single topography in MEG by adaptive sequential Monte Carlo samplers
A Sorrentino, G Luria, R Aramini
Inverse Problems 30 (4), 045010, 2014
A Simplex Method for the Calibration of a MEG Device
V Vivaldi, S Sommariva, A Sorrentino
Communications in Applied and Industrial Mathematics 10, 35-46, 2019
Dynamic filtering of static dipoles in magnetoencephalography
A Sorrentino, AM Johansen, JAD Aston, TE Nichols, WS Kendall
The annals of applied statistics, 955-988, 2013
Highly automated dipole estimation (HADES)
C Campi, A Pascarella, A Sorrentino, M Piana
Computational intelligence and neuroscience 2011 (1), 982185, 2011
Particle filters: a new method for reconstructing multiple current dipoles from MEG data
A Sorrentino, L Parkkonen, M Piana
International congress series 1300, 173-176, 2007
A comparative study of the robustness of frequency-domain connectivity measures to finite data length
S Sommariva, A Sorrentino, M Piana, V Pizzella, L Marzetti
Brain topography 32, 675-695, 2019
Modulation of brain and behavioural responses to cognitive visual stimuli with varying signal-to-noise ratios
A Sorrentino, L Parkkonen, M Piana, AM Massone, L Narici, S Carozzo, ...
Clinical Neurophysiology 117 (5), 1098-1105, 2006
A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data
A Sorrentino, A Sannino, N Spinelli, M Piana, A Boselli, V Tontodonato, ...
Atmospheric Measurement Techniques 15 (1), 149-164, 2022
Inverse Modeling for MEG/EEG data
A Sorrentino, M Piana
Mathematical and Theoretical Neuroscience: Cell, Network and Data Analysisá…, 2017
Statistical approaches to the inverse problem
A Pascarella, A Sorrentino
Magnetoencephalography, 93-112, 2011
Particle filtering, beamforming and multiple signal classification for the analysis of magnetoencephalography time series: a comparison of algorithms
A Pascarella, A Sorrentino, C Campi, M Piana
Inverse Problems and Imaging 4 (1), 169-170, 2010
Identification of multiple hard X-ray sources in solar flares: a Bayesian analysis of the 2002 February 20 event
F Sciacchitano, A Sorrentino, AG Emslie, AM Massone, M Piana
The Astrophysical Journal 862 (1), 68, 2018
Expectation maximization and the retrieval of the atmospheric extinction coefficients by inversion of Raman lidar data
S Garbarino, A Sorrentino, AM Massone, A Sannino, A Boselli, X Wang, ...
Optics Express 24 (19), 21497-21511, 2016
Bayesian smoothing of dipoles in magneto-/electroencephalography
V Vivaldi, A Sorrentino
Inverse Problems 32 (4), 045007, 2016
An in–vivo validation of ESI methods with focal sources
A Pascarella, E Mikulan, F Sciacchitano, S Sarasso, A Rubino, I Sartori, ...
NeuroImage 277, 120219, 2023
Il sistema al momento non pu˛ eseguire l'operazione. Riprova pi¨ tardi.
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