A Matlab-Based Open-Source Toolbox for Artefact Removal from Extracellular Neuronal Signals

Marcos Fabietti; Mufti Mahmud; Ahmad Lotfi

doi:10.1007/978-3-030-86993-9_32

A Matlab-Based Open-Source Toolbox for Artefact Removal from Extracellular Neuronal Signals

Marcos Fabietti
, Mufti Mahmud^*
, Ahmad Lotfi

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

The neural recordings in the form of local field potentials offer useful insights on higher-level neural functions by providing information about the activation and deactivation of neural circuits. But often these recordings are contaminated by multiple internal and external sources of noise from nearby electronic systems and body movements. However, to facilitate knowledge extraction from these recordings, identification and removal of the artefacts are empirical, and various computational techniques have been applied for this purpose. Here we report a new module for artefact removal, an extension of the toolbox named SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) which allows for fast application of deep learning techniques to remove said artefacts without relying on data from other channels.

Original language	English
Title of host publication	Brain Informatics - 14th International Conference, BI 2021, Proceedings
Editors	Mufti Mahmud, M Shamim Kaiser, Stefano Vassanelli, Qionghai Dai, Ning Zhong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	351-365
Number of pages	15
ISBN (Print)	9783030869922
DOIs	https://doi.org/10.1007/978-3-030-86993-9_32
State	Published - 2021
Externally published	Yes
Event	14th International Conference on Brain Informatics, BI 2021 - Virtual, Online Duration: 17 Sep 2021 → 19 Sep 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12960 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Brain Informatics, BI 2021
City	Virtual, Online
Period	17/09/21 → 19/09/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Artefact detection
Computational neuroscience
Machine learning

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-86993-9_32

Cite this

Fabietti, M., Mahmud, M., & Lotfi, A. (2021). A Matlab-Based Open-Source Toolbox for Artefact Removal from Extracellular Neuronal Signals. In M. Mahmud, M. S. Kaiser, S. Vassanelli, Q. Dai, & N. Zhong (Eds.), Brain Informatics - 14th International Conference, BI 2021, Proceedings (pp. 351-365). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12960 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-86993-9_32

Fabietti, Marcos ; Mahmud, Mufti ; Lotfi, Ahmad. / A Matlab-Based Open-Source Toolbox for Artefact Removal from Extracellular Neuronal Signals. Brain Informatics - 14th International Conference, BI 2021, Proceedings. editor / Mufti Mahmud ; M Shamim Kaiser ; Stefano Vassanelli ; Qionghai Dai ; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2021. pp. 351-365 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The neural recordings in the form of local field potentials offer useful insights on higher-level neural functions by providing information about the activation and deactivation of neural circuits. But often these recordings are contaminated by multiple internal and external sources of noise from nearby electronic systems and body movements. However, to facilitate knowledge extraction from these recordings, identification and removal of the artefacts are empirical, and various computational techniques have been applied for this purpose. Here we report a new module for artefact removal, an extension of the toolbox named SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) which allows for fast application of deep learning techniques to remove said artefacts without relying on data from other channels.",

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author = "Marcos Fabietti and Mufti Mahmud and Ahmad Lotfi",

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Fabietti, M, Mahmud, M & Lotfi, A 2021, A Matlab-Based Open-Source Toolbox for Artefact Removal from Extracellular Neuronal Signals. in M Mahmud, MS Kaiser, S Vassanelli, Q Dai & N Zhong (eds), Brain Informatics - 14th International Conference, BI 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12960 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 351-365, 14th International Conference on Brain Informatics, BI 2021, Virtual, Online, 17/09/21. https://doi.org/10.1007/978-3-030-86993-9_32

A Matlab-Based Open-Source Toolbox for Artefact Removal from Extracellular Neuronal Signals. / Fabietti, Marcos; Mahmud, Mufti; Lotfi, Ahmad.
Brain Informatics - 14th International Conference, BI 2021, Proceedings. ed. / Mufti Mahmud; M Shamim Kaiser; Stefano Vassanelli; Qionghai Dai; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2021. p. 351-365 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12960 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The neural recordings in the form of local field potentials offer useful insights on higher-level neural functions by providing information about the activation and deactivation of neural circuits. But often these recordings are contaminated by multiple internal and external sources of noise from nearby electronic systems and body movements. However, to facilitate knowledge extraction from these recordings, identification and removal of the artefacts are empirical, and various computational techniques have been applied for this purpose. Here we report a new module for artefact removal, an extension of the toolbox named SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) which allows for fast application of deep learning techniques to remove said artefacts without relying on data from other channels.

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Fabietti M, Mahmud M, Lotfi A. A Matlab-Based Open-Source Toolbox for Artefact Removal from Extracellular Neuronal Signals. In Mahmud M, Kaiser MS, Vassanelli S, Dai Q, Zhong N, editors, Brain Informatics - 14th International Conference, BI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 351-365. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-86993-9_32

A Matlab-Based Open-Source Toolbox for Artefact Removal from Extracellular Neuronal Signals

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this