On-Chip Machine Learning for Portable Systems: Application to Electroencephalography-based Brain-Computer Interfaces

Marcos Fabietti; Mufti Mahmud; Ahmad Lotfi

doi:10.1109/IJCNN52387.2021.9533413

On-Chip Machine Learning for Portable Systems: Application to Electroencephalography-based Brain-Computer Interfaces

Marcos Fabietti
, Mufti Mahmud^*
, Ahmad Lotfi

^*Corresponding author for this work

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

4 Scopus citations

Abstract

The improvement of hardware for the acquisition and processing of electroencephalography (EEG) has made its portability become a reality. This allows for studies to be carried outside lab settings, as well as many commercial applications. As recordings are done over extended periods, these devices generate large volumes of data, mainly if the neuronal activity is recorded through multiple channels. Machine learning (ML) techniques allow to effectively analyse and use this data for a wide range of applications. However the portability of these techniques can be challenging. In this article, we set out to review over 40 relevant articles where ML techniques in a diverse set of EEG applications that have successfully been incorporated into portable systems.

Original language	English
Title of host publication	IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9780738133669
DOIs	https://doi.org/10.1109/IJCNN52387.2021.9533413
State	Published - 18 Jul 2021
Externally published	Yes
Event	2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Online, China Duration: 18 Jul 2021 → 22 Jul 2021

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2021-July
ISSN (Print)	2161-4393
ISSN (Electronic)	2161-4407

Conference

Conference	2021 International Joint Conference on Neural Networks, IJCNN 2021
Country/Territory	China
City	Virtual, Online
Period	18/07/21 → 22/07/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

EEG
embedded systems
hardware processing
neural networks
signal processing

ASJC Scopus subject areas

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN52387.2021.9533413

Cite this

Fabietti, M., Mahmud, M., & Lotfi, A. (2021). On-Chip Machine Learning for Portable Systems: Application to Electroencephalography-based Brain-Computer Interfaces. In IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN52387.2021.9533413

Fabietti, Marcos ; Mahmud, Mufti ; Lotfi, Ahmad. / On-Chip Machine Learning for Portable Systems : Application to Electroencephalography-based Brain-Computer Interfaces. IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the International Joint Conference on Neural Networks).

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title = "On-Chip Machine Learning for Portable Systems: Application to Electroencephalography-based Brain-Computer Interfaces",

abstract = "The improvement of hardware for the acquisition and processing of electroencephalography (EEG) has made its portability become a reality. This allows for studies to be carried outside lab settings, as well as many commercial applications. As recordings are done over extended periods, these devices generate large volumes of data, mainly if the neuronal activity is recorded through multiple channels. Machine learning (ML) techniques allow to effectively analyse and use this data for a wide range of applications. However the portability of these techniques can be challenging. In this article, we set out to review over 40 relevant articles where ML techniques in a diverse set of EEG applications that have successfully been incorporated into portable systems.",

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

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Joint Conference on Neural Networks, IJCNN 2021 ; Conference date: 18-07-2021 Through 22-07-2021",

year = "2021",

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doi = "10.1109/IJCNN52387.2021.9533413",

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Fabietti, M, Mahmud, M & Lotfi, A 2021, On-Chip Machine Learning for Portable Systems: Application to Electroencephalography-based Brain-Computer Interfaces. in IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Proceedings of the International Joint Conference on Neural Networks, vol. 2021-July, Institute of Electrical and Electronics Engineers Inc., 2021 International Joint Conference on Neural Networks, IJCNN 2021, Virtual, Online, China, 18/07/21. https://doi.org/10.1109/IJCNN52387.2021.9533413

On-Chip Machine Learning for Portable Systems: Application to Electroencephalography-based Brain-Computer Interfaces. / Fabietti, Marcos; Mahmud, Mufti; Lotfi, Ahmad.
IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July).

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

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AB - The improvement of hardware for the acquisition and processing of electroencephalography (EEG) has made its portability become a reality. This allows for studies to be carried outside lab settings, as well as many commercial applications. As recordings are done over extended periods, these devices generate large volumes of data, mainly if the neuronal activity is recorded through multiple channels. Machine learning (ML) techniques allow to effectively analyse and use this data for a wide range of applications. However the portability of these techniques can be challenging. In this article, we set out to review over 40 relevant articles where ML techniques in a diverse set of EEG applications that have successfully been incorporated into portable systems.

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KW - embedded systems

KW - hardware processing

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KW - signal processing

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Fabietti M, Mahmud M, Lotfi A. On-Chip Machine Learning for Portable Systems: Application to Electroencephalography-based Brain-Computer Interfaces. In IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN52387.2021.9533413

On-Chip Machine Learning for Portable Systems: Application to Electroencephalography-based Brain-Computer Interfaces

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this