Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes

Marcos Fabietti; Mufti Mahmud; Ahmad Lotfi; Alberto Avarua; David Gugganmos; Randolph Xudo; Michela Chiappalone

doi:10.1109/IJCNN48605.2020.9207320

Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes

Marcos Fabietti, Mufti Mahmud, Ahmad Lotfi^*, Alberto Avarua, David Gugganmos, Randolph Xudo, Michela Chiappalone

^*Corresponding author for this work

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

45 Scopus citations

Abstract

The neural recordings known as Local Field Potentials (LFPs) provide important information on how neural circuits operate and relate. Due to the involvement of complex electronic apparatuses in the recording setups, these signals are often significantly contaminated by artifacts generated by a number of internal and external sources. To make the best use of these signals, it is imperative to detect and remove the artifacts from these signals. Hence, this work proposes a pattern recognition neural network based single-channel automatic artifact detection tool. The tool is capable of detecting the artifacts with an 93.2% of overall accuracy and requires an average computing time of 2.57 seconds to analyse LFPs of one minute duration, making it a strong candidate for online deployment without the need for employing high performance computing equipment.

Original language	English
Title of host publication	2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728169262
DOIs	https://doi.org/10.1109/IJCNN48605.2020.9207320
State	Published - Jul 2020
Externally published	Yes
Event	2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2020 International Joint Conference on Neural Networks, IJCNN 2020
Country/Territory	United Kingdom
City	Virtual, Glasgow
Period	19/07/20 → 24/07/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Computational neuroscience
machine learning
physiological signals

ASJC Scopus subject areas

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN48605.2020.9207320

Cite this

Fabietti, M., Mahmud, M., Lotfi, A., Avarua, A., Gugganmos, D., Xudo, R., & Chiappalone, M. (2020). Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes. In 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings Article 9207320 (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN48605.2020.9207320

Fabietti, Marcos ; Mahmud, Mufti ; Lotfi, Ahmad et al. / Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes. 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. (Proceedings of the International Joint Conference on Neural Networks).

@inproceedings{b4b3bfa53cf047e5a7a36e08cacc8a2e,

title = "Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes",

abstract = "The neural recordings known as Local Field Potentials (LFPs) provide important information on how neural circuits operate and relate. Due to the involvement of complex electronic apparatuses in the recording setups, these signals are often significantly contaminated by artifacts generated by a number of internal and external sources. To make the best use of these signals, it is imperative to detect and remove the artifacts from these signals. Hence, this work proposes a pattern recognition neural network based single-channel automatic artifact detection tool. The tool is capable of detecting the artifacts with an 93.2\% of overall accuracy and requires an average computing time of 2.57 seconds to analyse LFPs of one minute duration, making it a strong candidate for online deployment without the need for employing high performance computing equipment.",

keywords = "Computational neuroscience, machine learning, physiological signals",

author = "Marcos Fabietti and Mufti Mahmud and Ahmad Lotfi and Alberto Avarua and David Gugganmos and Randolph Xudo and Michela Chiappalone",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Joint Conference on Neural Networks, IJCNN 2020 ; Conference date: 19-07-2020 Through 24-07-2020",

year = "2020",

month = jul,

doi = "10.1109/IJCNN48605.2020.9207320",

language = "English",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Fabietti, M, Mahmud, M, Lotfi, A, Avarua, A, Gugganmos, D, Xudo, R & Chiappalone, M 2020, Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes. in 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings., 9207320, Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., 2020 International Joint Conference on Neural Networks, IJCNN 2020, Virtual, Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/IJCNN48605.2020.9207320

Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes. / Fabietti, Marcos; Mahmud, Mufti; Lotfi, Ahmad et al.
2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9207320 (Proceedings of the International Joint Conference on Neural Networks).

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

TY - GEN

T1 - Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes

AU - Fabietti, Marcos

AU - Mahmud, Mufti

AU - Lotfi, Ahmad

AU - Avarua, Alberto

AU - Gugganmos, David

AU - Xudo, Randolph

AU - Chiappalone, Michela

PY - 2020/7

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N2 - The neural recordings known as Local Field Potentials (LFPs) provide important information on how neural circuits operate and relate. Due to the involvement of complex electronic apparatuses in the recording setups, these signals are often significantly contaminated by artifacts generated by a number of internal and external sources. To make the best use of these signals, it is imperative to detect and remove the artifacts from these signals. Hence, this work proposes a pattern recognition neural network based single-channel automatic artifact detection tool. The tool is capable of detecting the artifacts with an 93.2% of overall accuracy and requires an average computing time of 2.57 seconds to analyse LFPs of one minute duration, making it a strong candidate for online deployment without the need for employing high performance computing equipment.

AB - The neural recordings known as Local Field Potentials (LFPs) provide important information on how neural circuits operate and relate. Due to the involvement of complex electronic apparatuses in the recording setups, these signals are often significantly contaminated by artifacts generated by a number of internal and external sources. To make the best use of these signals, it is imperative to detect and remove the artifacts from these signals. Hence, this work proposes a pattern recognition neural network based single-channel automatic artifact detection tool. The tool is capable of detecting the artifacts with an 93.2% of overall accuracy and requires an average computing time of 2.57 seconds to analyse LFPs of one minute duration, making it a strong candidate for online deployment without the need for employing high performance computing equipment.

KW - Computational neuroscience

KW - machine learning

KW - physiological signals

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M3 - Conference contribution

AN - SCOPUS:85093820829

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Fabietti M, Mahmud M, Lotfi A, Avarua A, Gugganmos D, Xudo R et al. Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes. In 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9207320. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN48605.2020.9207320

Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this