Alberto Sorrentino
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
A Rao–Blackwellized particle filter for magnetoencephalography
C Campi, A Pascarella, A Sorrentino, M Piana
Inverse Problems 24 (2), 025023, 2008
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
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
Highly automated dipole estimation (HADES)
C Campi, A Pascarella, A Sorrentino, M Piana
Computational intelligence and neuroscience 2011, 2011
Sequential Monte Carlo samplers for semi-linear inverse problems and application to magnetoencephalography
S Sommariva, A Sorrentino
Inverse Problems 30 (11), 114020, 2014
Dynamic filtering of static dipoles in magnetoencephalography
A Sorrentino, AM Johansen, JAD Aston, TE Nichols, WS Kendall
The annals of applied statistics 7 (2), 955-988, 2013
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
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
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 & Imaging 4 (1), 169, 2010
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 (4), 675-695, 2019
Bayesian smoothing of dipoles in magneto-/electroencephalography
V Vivaldi, A Sorrentino
Inverse Problems 32 (4), 045007, 2016
Statistical approaches to the inverse problem
A Pascarella, A Sorrentino
Magnetoencephalography, 93-112, 2011
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
Particle filters for magnetoencephalography
A Sorrentino
Archives of Computational Methods in Engineering 17 (3), 213-251, 2010
Particle filters and rap-music in meg source modelling: a comparison
A Pascarella, A Sorrentino, M Piana, L Parkkonen
International congress series 1300, 161-164, 2007
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), 1-8, 2020
Inverse Modeling for MEG/EEG data
A Sorrentino, M Piana
Mathematical and Theoretical Neuroscience, 239-253, 2017
Bayesian multi-dipole modelling in the frequency domain
G Luria, D Duran, E Visani, S Sommariva, F Rotondi, DR Sebastiano, ...
Journal of neuroscience methods 312, 27-36, 2019
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
Системата не може да изпълни операцията сега. Опитайте отново по-късно.
Статии 1–20