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dc.contributor.author
Wyser, Dominik
dc.contributor.supervisor
Gassert, Roger
dc.contributor.supervisor
Wolf, Martin Peter
dc.contributor.supervisor
Tachtsidis, Ilias
dc.date.accessioned
2022-02-11T12:12:54Z
dc.date.available
2021-02-09T20:10:17Z
dc.date.available
2021-02-10T08:19:12Z
dc.date.available
2022-02-11T12:12:54Z
dc.date.issued
2020
dc.identifier.uri
http://hdl.handle.net/20.500.11850/468730
dc.identifier.doi
10.3929/ethz-b-000468730
dc.description.abstract
The brain is a fascinating organ, and we often only realize its importance when its functionality becomes limited. Neuroimaging has greatly advanced our understanding of how the brain is organized and operates, and especially non-invasive neuroimaging techniques have become essential clinical elements to link brain disorders and behavioral consequences. Usually, non-invasive neuroimaging devices are bulky, expensive, and stationary, but recent technical advances enable the development of wearable and low-cost devices. Wearable neuroimaging devices promise an extension of the conventional approaches by allowing them to observe brain activity in everyday environments and activities. Furthermore, it could enable personalize therapies for neurologically impaired persons and help them to regain more independence during their daily lives when applied in a brain-computer interface (BCI) setting, where a robotic device is controlled from brain recordings. Functional near-infrared spectroscopy (fNIRS) is a promising technology that employs light to infer changes in blood flow in the brain, offering the potential to be applied in home environments. However, reasons hampering the widespread use of fNIRS exist. Firstly, fNIRS is susceptible to changes in blood flow in the brain and scalp, with the latter reducing the reliability of the estimated brain activity. Secondly, the existing wearable fNIRS instruments suffer from technical shortcomings, as they are bulky and heavy, as well as unable to record scalp blood flow, which is a prerequisite for advanced signal processing of fNIRS measurements. In this thesis, we aim at driving the transition of neuroimaging from research to home application by promoting miniaturized and wearable fNIRS, while at the same time investigating ways to maximize sensitivity to brain activity. To reach this goal, we have defined three aims: 1) Development and validation of a wearable fNIRS system to acquire brain activity from sensorimotor brain areas that allows for multi-distance measurements, 2) Exploration and implementation of novel algorithms to improve estimates of brain activity, and 3) In-vivo experimental evaluation to decode sensorimotor activity. To address these aims, we have developed a wearable fNIRS system, optoHIVE, which offers promising performance from the utilization of silicon photomultipliers, a high degree of modularity, multi-channel and multi-wavelength measurements, and miniaturization. The validity of optoHIVE was proven in in-vitro and in-vivo measurements, including the testing on 33 healthy subjects. A new algorithm was proposed to optimize sensitivity to the estimated brain activity by removing disturbing signals from the scalp, enhancing the estimates up to 100\% in comparison to state-of-the-art approaches. The gained knowledge allowed the transfer from offline to real-time algorithms to address time-critical applications such as BCI applications. Additionally, a high reliability of brain activity estimates was demonstrated when making use of the short-channels, and factors that reduce reproducibility were determined. The classification of left and right hand grasping was performed with an average accuracy of 85\%, being in the same range as state-of-the-art studies. We conclude that the fully functional optoHIVE device was successfully developed and tested for the purpose of transferring fNIRS to in-home environments. This serves as proof that wearable and high-performance fNIRS instruments can be developed, opening new avenues for research, clinical and out-of-the-lab applications. We determined brain patterns during a hand grasping study, confirming the ability to detect spatially specific brain activity. Further, we showed that with more optimal algorithms, estimates of brain activity become more reproducible, which is a vital prerequisite to foster trustworthiness of researchers, clinicians and patients into the fNIRS technology. However, before wearable neuroimaging can make the step into our daily lives, further improvements regarding robustness and usability of fNIRS instruments, as well as the reproducibility of brain activity estimates, need to be addressed. This thesis serves as a foundation for fNIRS to make the transition from research and clinical applications into home environments. Possible applications of optoHIVE include bedside or in-home brain monitoring, or assistance for stroke patients during activities of daily living based on a BCI setting. The future of fNIRS-based neuroimaging is expected to lie in multi-modality, for example, by combining it with electrical recordings from the brain/muscles, or to apply it together with transcranial magnetic stimulation (i.e., neuromodulation).
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
fNIRS
en_US
dc.subject
Neuroscience
en_US
dc.subject
Brain
en_US
dc.subject
Brain imaging
en_US
dc.subject
Brain computer interface (BCI)
en_US
dc.subject
Rehabilitation
en_US
dc.subject
Stroke
en_US
dc.subject
neuroimaging
en_US
dc.title
Towards FNIRS-Based Monitoring of Brain Activity in Daily Life: Development and Evaluation of Optohive
en_US
dc.type
Doctoral Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2021-02-10
ethz.size
215 p.
en_US
ethz.code.ddc
DDC - DDC::6 - Technology, medicine and applied sciences::610 - Medical sciences, medicine
en_US
ethz.code.ddc
DDC - DDC::6 - Technology, medicine and applied sciences::600 - Technology (applied sciences)
en_US
ethz.identifier.diss
26819
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::03827 - Gassert, Roger / Gassert, Roger
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::03827 - Gassert, Roger / Gassert, Roger
en_US
ethz.tag
fNIRS
en_US
ethz.tag
Brain–machine interface
en_US
ethz.tag
Brain
en_US
ethz.date.deposited
2021-02-09T20:10:44Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.date.embargoend
2022-02-10
ethz.rosetta.installDate
2021-02-10T08:19:26Z
ethz.rosetta.lastUpdated
2022-03-29T18:46:19Z
ethz.rosetta.versionExported
true
ethz.COinS
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