A perspective on electroencephalography sensors for brain-computer interfaces

Iacopi, F; Lin, C-T

A perspective on electroencephalography sensors for brain-computer interfaces

Iacopi, F

Lin, C-T

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Publisher:: IOP Publishing
Publication Type:: Journal Article
Citation:: Progress in Biomedical Engineering, 2022
Issue Date:: 2022-10-11

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Field	Value	Language
dc.contributor.author	Iacopi, F https://orcid.org/0000-0002-3196-0990
dc.contributor.author	Lin, C-T
dc.date.accessioned	2022-10-24T04:35:12Z
dc.date.available	2022-10-24T04:35:12Z
dc.date.issued	2022-10-11
dc.identifier.citation	Progress in Biomedical Engineering, 2022
dc.identifier.issn	2516-1091
dc.identifier.issn	2516-1091
dc.identifier.uri	http://hdl.handle.net/10453/162624
dc.description.abstract	<jats:title>Abstract</jats:title> <jats:p>This Perspective offers a concise overview of the current state-of-the-art of neural sensors for brain-machine interfaces, with particular attention towards brain-controlled robotics. We first describe current approaches, decoding models and associated choice of common paradigms, and their relation to the position and requirements of the neural sensors. While implanted intracortical sensors offer unparalleled spatial, temporal and frequency resolution, the risks related to surgery and post-surgery complications pose a significant barrier to deployment beyond severely disabled individuals. For less critical and larger scale applications, we emphasize the need to further develop dry scalp electroencephalography (EEG) sensors as non-invasive probes with high sensitivity, accuracy, comfort and robustness for prolonged and repeated use. In particular, as many of the employed paradigms require placing EEG sensors in hairy areas of the scalp, ensuring the aforementioned requirements becomes particularly challenging. Nevertheless, neural sensing technologies in this area are accelerating, also thanks to the advancement of miniaturised technologies and the engineering of novel biocompatible nanomaterials. The development of novel multifunctional nanomaterials is also expected to enable the integration of redundancy by probing the same type of information through different mechanisms for increased accuracy, as well as the integration of complementary and synergetic functions that could range from the monitoring of physiological states to incorporating optical imaging. </jats:p>
dc.language	en
dc.publisher	IOP Publishing
dc.relation.ispartof	Progress in Biomedical Engineering
dc.relation.isbasedon	10.1088/2516-1091/ac993d
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	A perspective on electroencephalography sensors for brain-computer interfaces
dc.type	Journal Article
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-10-11T00:00:00+1000Z
dc.date.updated	2022-10-24T04:35:10Z
pubs.publication-status	Published online

Abstract:

Abstract This Perspective offers a concise overview of the current state-of-the-art of neural sensors for brain-machine interfaces, with particular attention towards brain-controlled robotics. We first describe current approaches, decoding models and associated choice of common paradigms, and their relation to the position and requirements of the neural sensors. While implanted intracortical sensors offer unparalleled spatial, temporal and frequency resolution, the risks related to surgery and post-surgery complications pose a significant barrier to deployment beyond severely disabled individuals. For less critical and larger scale applications, we emphasize the need to further develop dry scalp electroencephalography (EEG) sensors as non-invasive probes with high sensitivity, accuracy, comfort and robustness for prolonged and repeated use. In particular, as many of the employed paradigms require placing EEG sensors in hairy areas of the scalp, ensuring the aforementioned requirements becomes particularly challenging. Nevertheless, neural sensing technologies in this area are accelerating, also thanks to the advancement of miniaturised technologies and the engineering of novel biocompatible nanomaterials. The development of novel multifunctional nanomaterials is also expected to enable the integration of redundancy by probing the same type of information through different mechanisms for increased accuracy, as well as the integration of complementary and synergetic functions that could range from the monitoring of physiological states to incorporating optical imaging.

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http://hdl.handle.net/10453/162624