Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials.

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals / M. Fabietti, M. Mahmud, A. Lotfi, M.S. Kaiser, A. Averna, D.J. Guggenmos, R.J. Nudo, M. Chiappalone, J. Chen. - In: BRAIN INFORMATICS. - ISSN 2198-4026. - 8:1(2021 Jul 20), pp. 14.1-14.19. [10.1186/s40708-021-00135-3]

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

A. Averna;
2021

Abstract

Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials.
Artifacts; Local field potential; Machine learning; Neural networks; Neuronal signals
Settore ING-INF/06 - Bioingegneria Elettronica e Informatica
Settore MED/26 - Neurologia
Article (author)
File in questo prodotto:
File Dimensione Formato  
s40708-021-00135-3.pdf

accesso aperto

Tipologia: Publisher's version/PDF
Dimensione 3.58 MB
Formato Adobe PDF
3.58 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/873402
Citazioni
  • ???jsp.display-item.citation.pmc??? 2
  • Scopus 6
  • ???jsp.display-item.citation.isi??? ND
social impact