Integrated Sensing Devices for Brain-Computer Interfaces

Nguyen Do, TT; Hanh Duong, NM; Lin, CT

Integrated Sensing Devices for Brain-Computer Interfaces

Nguyen Do, TT Hanh Duong, NM Lin, CT

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: More-than-Moore Devices and Integration for Semi Conductors, 2023, pp. 241-258
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Nguyen Do, TT
dc.contributor.author	Hanh Duong, NM
dc.contributor.author	Lin, CT
dc.date.accessioned	2023-11-13T00:35:10Z
dc.date.available	2023-11-13T00:35:10Z
dc.date.issued	2023-01-01
dc.identifier.citation	More-than-Moore Devices and Integration for Semi Conductors, 2023, pp. 241-258
dc.identifier.isbn	9783031216091
dc.identifier.uri	http://hdl.handle.net/10453/173359
dc.description.abstract	Brain-computer interfaces (BCI) help users to interact with machines via brain activity and without the use of muscles. Among the many components of BCI frameworks, sensor technology helps to make the systems highly efficient and robust. As a source of input for BCI systems, sensors also provide high-quality signals that can support downstream tasks, such as noise removal and feature extraction. This book chapter explores the fundamental concepts and configurations of the non-invasive sensor technology that are commonly used in BCI systems.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	More-than-Moore Devices and Integration for Semi Conductors
dc.relation.isbasedon	10.1007/978-3-031-21610-7_7
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Integrated Sensing Devices for Brain-Computer Interfaces
dc.type	Chapter
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 Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-11-13T00:35:09Z
pubs.publication-status	Published

Abstract:

Brain-computer interfaces (BCI) help users to interact with machines via brain activity and without the use of muscles. Among the many components of BCI frameworks, sensor technology helps to make the systems highly efficient and robust. As a source of input for BCI systems, sensors also provide high-quality signals that can support downstream tasks, such as noise removal and feature extraction. This book chapter explores the fundamental concepts and configurations of the non-invasive sensor technology that are commonly used in BCI systems.

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http://hdl.handle.net/10453/173359