Author

Fabio Ferrarelli

University of PittsburghORCID 0000-0003-0560-0227Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:benchmarking· 2 Assay:electronic health records· 2 Organism:human· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Marcello MassiminiiD· 2 shared Mario RosanovaiD· 2 shared Matteo FecchioiD· 2 shared Simone RussoiD· 2 shared Abraham NunesiD· 1 shared Adenauer Girardi CasaliiD· 1 shared Ahmad MayeliiD· 1 shared Ahmadreza KeihaniiD· 1 shared

Activity by Year

2017-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersNeuroscience

Marcello MassiminiORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

NeuroscienceNeuroImagePeer-reviewedFeb 12, 2026

Recognizing EEG responses to active TMS vs. sham stimulations in different TMS-EEG datasets: a machine learning approach

Ahmadreza Keihani, Francesco Luciano Donati, Simone Russo…·Fabio Ferrarelli

This study uses a BiLSTM machine learning model to distinguish TMS-evoked EEG responses from sham stimulations across multiple datasets, achieving high accuracy with few trials.

3 citationstop 3%

humanelectronic health records

𝕏 PostBluesky

NeurosciencebioRxivPreprintJan 16, 2023

Bridging Big Data: Procedures for Combining Non-equivalent Cognitive Measures from the ENIGMA Consortium

Eamonn Kennedy, Shashank Vadlamani, Hannah Michelle Lindsey…·Marise B. Parent

The paper demonstrates a data harmonization process for memory testing across 53 studies, reducing inter-site variance while preserving covariate effects.

6 citations

statistical modelbenchmarking

𝕏 PostBluesky

NeurosciencePLoS ONEPeer-reviewedSep 14, 2017

The Spectral Features Of EEG Responses To Transcranial Magnetic Stimulation Of The Primary Motor Cortex Depend On The Amplitude Of The Motor Evoked Potentials

Matteo Fecchio, Andrea Pigorini, Angela Comanducci…·Mario Rosanova

This study investigates how the spectral features of TEPs from M1 TMS depend on MEP amplitude, finding a late ERD linked to MEP strength.

155 citationstop 2%

humanelectronic health records

𝕏 PostBluesky