Extracting Motion-Related Subspaces from EEG in Mobile Brain/Body Imaging Studies using Source Power Comodulation

Gehrke, L; Guerdan, L; Gramann, K

Extracting Motion-Related Subspaces from EEG in Mobile Brain/Body Imaging Studies using Source Power Comodulation

Gehrke, L Guerdan, L Gramann, K

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Publication Type:: Conference Proceeding
Citation:: International IEEE/EMBS Conference on Neural Engineering, NER, 2019, 2019-March pp. 344 - 347
Issue Date:: 2019-05-16

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Field	Value	Language
dc.contributor.author	Gehrke, L	en_US
dc.contributor.author	Guerdan, L	en_US
dc.contributor.author	Gramann, K https://orcid.org/0000-0003-2673-1832	en_US
dc.date.issued	2019-05-16	en_US
dc.identifier.citation	International IEEE/EMBS Conference on Neural Engineering, NER, 2019, 2019-March pp. 344 - 347	en_US
dc.identifier.isbn	9781538679210	en_US
dc.identifier.issn	1948-3546	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136352
dc.description.abstract	© 2019 IEEE. Mobile Brain/Body Imaging (MoBI) is an emerging non-invasive approach to investigate human brain activity and motor behavior associated with cognitive processes in natural conditions. MoBI studies and analyses pipelines combine brain measurements, e.g. Electroencephalography (EEG), with motion data as participants conduct tasks with near-natural behavior. Within the field however, standard source decomposition and reconstruction pipelines largely rely on unsupervised blind source separation (BSS) approaches and do not consider movement information to guide the decomposition of oscillatory brain sources. We propose the use of a supervised spatial filtering method, Source Power Co-modulation (SPoC), for extracting source components that co-modulate with body motion. Further, we introduce a method to validate the quality of oscillatory sources in MoBI studies. We illustrate the approach to investigate the link between hand and head movement kinematics and power dynamics of EEG sources while participants explore an invisible maze in virtual reality. Stable oscillatory source envelopes correlating with hand and head motion were isolated in all subjects, with median ρ =.13 for all sources and median ρ =.16 for sources passing the selection criteria. The results indicate that it is possible to improve movement related source separation to further guide our understanding of how movement and brain dynamics interact.	en_US
dc.relation.ispartof	International IEEE/EMBS Conference on Neural Engineering, NER	en_US
dc.relation.isbasedon	10.1109/NER.2019.8717157	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Extracting Motion-Related Subspaces from EEG in Mobile Brain/Body Imaging Studies using Source Power Comodulation	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2019-March	en_US
pubs.embargo.period	Not known	en_US
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 Computer Science
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	2019-March	en_US

Abstract:

© 2019 IEEE. Mobile Brain/Body Imaging (MoBI) is an emerging non-invasive approach to investigate human brain activity and motor behavior associated with cognitive processes in natural conditions. MoBI studies and analyses pipelines combine brain measurements, e.g. Electroencephalography (EEG), with motion data as participants conduct tasks with near-natural behavior. Within the field however, standard source decomposition and reconstruction pipelines largely rely on unsupervised blind source separation (BSS) approaches and do not consider movement information to guide the decomposition of oscillatory brain sources. We propose the use of a supervised spatial filtering method, Source Power Co-modulation (SPoC), for extracting source components that co-modulate with body motion. Further, we introduce a method to validate the quality of oscillatory sources in MoBI studies. We illustrate the approach to investigate the link between hand and head movement kinematics and power dynamics of EEG sources while participants explore an invisible maze in virtual reality. Stable oscillatory source envelopes correlating with hand and head motion were isolated in all subjects, with median ρ =.13 for all sources and median ρ =.16 for sources passing the selection criteria. The results indicate that it is possible to improve movement related source separation to further guide our understanding of how movement and brain dynamics interact.

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http://hdl.handle.net/10453/136352