Temporal representation of Motor Imagery : towards improved Brain-Computer Interface-based strokerehabilitation

Practicing Motor Imagery (MI) with a Brain-Computer Interface (BCI) has shown promise in promoting motor recovery in stroke patients. A BCI records a person’s brain activity and provides feedback to the person in real time, which allows the person to practice his or her brain activity. By imagining...

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Main Author: Tidare, Jonatan
Format: Others
Language:English
Published: Mälardalens högskola, Inbyggda system 2021
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-53082
http://nbn-resolving.de/urn:isbn:978-91-7485-495-4
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spelling ndltd-UPSALLA1-oai-DiVA.org-mdh-530822021-02-06T05:28:01ZTemporal representation of Motor Imagery : towards improved Brain-Computer Interface-based strokerehabilitationengTidare, JonatanMälardalens högskola, Inbyggda systemVästerås : Mälardalen University2021brain-computer interfaceeletroencephalogramstroke rehabilitationBiomedical Laboratory Science/TechnologyBiomedicinsk laboratorievetenskap/teknologiPracticing Motor Imagery (MI) with a Brain-Computer Interface (BCI) has shown promise in promoting motor recovery in stroke patients. A BCI records a person’s brain activity and provides feedback to the person in real time, which allows the person to practice his or her brain activity. By imagining a movement (performing MI) such as gripping with their hand, cortical areas in the brain are activated that largely overlaps with those activated during the actual hand movement. A BCI can provide positive feedback when the hand-related cortical areas are activated during MI, which helps a person to learn how to perform MI. Despite evidence that stroke patients may recover some motor function from practicing MI with BCI feedback thanks to the feedback provided from a BCI, the effectiveness and reliability of BCI-based rehabilitation are still poor.  A BCI can detect MI by analyzing patterns of features from the brain activity. The most common features are extracted from the oscillatory activity in the brain.  In BCI research, MI is often treated as a static pattern of features, which is detected by using machine learning algorithms to assign activity into a binary state. However, this model of MI may be inaccurate. Analyzing brain activity as dynamically varying over time and with a continuous measure of strength could better represent the cortical activity related to MI.  In this Licentiate thesis, I explore a method for analyzing the temporal dynamic of MI-activity with a continuous measure of strength. Brain activity was recorded with electroencephalography (EEG) and subject-specific feature patterns were extracted from a group of healthy subjects while they performed MI of two opposing hand movements: opening and closing the hand. Although MI of the two same-hand movements could not be discriminated, the continuous output from a machine learning algorithm was shown to correlate well with MI-related feature patterns. The temporal analysis also revealed that MI is dynamically encoded early, but later stabilizes into a more static pattern of brain activity. Last, to accommodate for higher temporal resolution of MI, I designed and evaluated a BCI framework by its feedback delay and uncertainty as a function of the stress on the system and found a non-linear correlation. These results could be essential for developing a BCI with time-critical feedback. To summarize, in this Licentiate thesis I propose a promising method for analyzing and extracting a temporal representation of MI, enabling relevant and continuous neurofeedback which may contribute to clinical advances in BCI-based stroke rehabilitation. Licentiate thesis, comprehensive summaryinfo:eu-repo/semantics/masterThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-53082urn:isbn:978-91-7485-495-4Mälardalen University Press Licentiate Theses, 1651-9256 ; 301application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic brain-computer interface
eletroencephalogram
stroke rehabilitation
Biomedical Laboratory Science/Technology
Biomedicinsk laboratorievetenskap/teknologi
spellingShingle brain-computer interface
eletroencephalogram
stroke rehabilitation
Biomedical Laboratory Science/Technology
Biomedicinsk laboratorievetenskap/teknologi
Tidare, Jonatan
Temporal representation of Motor Imagery : towards improved Brain-Computer Interface-based strokerehabilitation
description Practicing Motor Imagery (MI) with a Brain-Computer Interface (BCI) has shown promise in promoting motor recovery in stroke patients. A BCI records a person’s brain activity and provides feedback to the person in real time, which allows the person to practice his or her brain activity. By imagining a movement (performing MI) such as gripping with their hand, cortical areas in the brain are activated that largely overlaps with those activated during the actual hand movement. A BCI can provide positive feedback when the hand-related cortical areas are activated during MI, which helps a person to learn how to perform MI. Despite evidence that stroke patients may recover some motor function from practicing MI with BCI feedback thanks to the feedback provided from a BCI, the effectiveness and reliability of BCI-based rehabilitation are still poor.  A BCI can detect MI by analyzing patterns of features from the brain activity. The most common features are extracted from the oscillatory activity in the brain.  In BCI research, MI is often treated as a static pattern of features, which is detected by using machine learning algorithms to assign activity into a binary state. However, this model of MI may be inaccurate. Analyzing brain activity as dynamically varying over time and with a continuous measure of strength could better represent the cortical activity related to MI.  In this Licentiate thesis, I explore a method for analyzing the temporal dynamic of MI-activity with a continuous measure of strength. Brain activity was recorded with electroencephalography (EEG) and subject-specific feature patterns were extracted from a group of healthy subjects while they performed MI of two opposing hand movements: opening and closing the hand. Although MI of the two same-hand movements could not be discriminated, the continuous output from a machine learning algorithm was shown to correlate well with MI-related feature patterns. The temporal analysis also revealed that MI is dynamically encoded early, but later stabilizes into a more static pattern of brain activity. Last, to accommodate for higher temporal resolution of MI, I designed and evaluated a BCI framework by its feedback delay and uncertainty as a function of the stress on the system and found a non-linear correlation. These results could be essential for developing a BCI with time-critical feedback. To summarize, in this Licentiate thesis I propose a promising method for analyzing and extracting a temporal representation of MI, enabling relevant and continuous neurofeedback which may contribute to clinical advances in BCI-based stroke rehabilitation.
author Tidare, Jonatan
author_facet Tidare, Jonatan
author_sort Tidare, Jonatan
title Temporal representation of Motor Imagery : towards improved Brain-Computer Interface-based strokerehabilitation
title_short Temporal representation of Motor Imagery : towards improved Brain-Computer Interface-based strokerehabilitation
title_full Temporal representation of Motor Imagery : towards improved Brain-Computer Interface-based strokerehabilitation
title_fullStr Temporal representation of Motor Imagery : towards improved Brain-Computer Interface-based strokerehabilitation
title_full_unstemmed Temporal representation of Motor Imagery : towards improved Brain-Computer Interface-based strokerehabilitation
title_sort temporal representation of motor imagery : towards improved brain-computer interface-based strokerehabilitation
publisher Mälardalens högskola, Inbyggda system
publishDate 2021
url http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-53082
http://nbn-resolving.de/urn:isbn:978-91-7485-495-4
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