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The Use of an MEG/fMRI-Compatible Finger Motion Sensor in Detecting Different Finger Actions

This paper explores the use of a novel device in detecting different finger actions among healthy individuals and individuals with stroke. The device is magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) compatible. It was prototyped to have four air-filled chambers that a...

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Detalles Bibliográficos
Autores principales: Yong, Xinyi, Li, Yasong, Menon, Carlo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4707295/
https://www.ncbi.nlm.nih.gov/pubmed/26793701
http://dx.doi.org/10.3389/fbioe.2015.00205
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author Yong, Xinyi
Li, Yasong
Menon, Carlo
author_facet Yong, Xinyi
Li, Yasong
Menon, Carlo
author_sort Yong, Xinyi
collection PubMed
description This paper explores the use of a novel device in detecting different finger actions among healthy individuals and individuals with stroke. The device is magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) compatible. It was prototyped to have four air-filled chambers that are made of silicone elastomer, which contains low magnetizing materials. When an individual compresses the device with his/her fingers, each chamber experiences a change in pressure, which is detected by a pressure sensor. In a previous recent work, our device was shown to be MEG/fMRI compatible. In this study, our research effort focuses on using the device to detect different finger actions (e.g., grasping and pinching) in non-shielded rooms. This is achieved by applying a support vector machine to the sensor data collected from the device when participants are resting and executing the different finger actions. The total number of possible finger actions that can be executed using the device is 31. The healthy participants could perform all the 31 different finger actions and the average classification accuracy achieved is 95.53 ± 2.63%. The stroke participants could perform all the 31 different finger actions with their healthy hand and the average classification accuracy achieved is 83.13 ± 6.69%. Unfortunately, the functions of their affected hands are compromised due to stroke. Thus, the number of finger actions they could perform ranges from 2 to 24, depending on the level of impairments. The average classification accuracy for the affected hand is 83.99 ± 16.38%. The ability to identify different finger actions using the device can provide a mean to researchers to label the data automatically in MEG/fMRI studies. In addition, the sensor data acquired from the device provide sensorimotor-­related information, such as speed and force, when the device is compressed. Thus, brain activations can be correlated with this information during different finger actions. Finally, the device can be used to assess the recovery of the sensory and motor functions of individuals with stroke when paired with fMRI.
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spelling pubmed-47072952016-01-20 The Use of an MEG/fMRI-Compatible Finger Motion Sensor in Detecting Different Finger Actions Yong, Xinyi Li, Yasong Menon, Carlo Front Bioeng Biotechnol Bioengineering and Biotechnology This paper explores the use of a novel device in detecting different finger actions among healthy individuals and individuals with stroke. The device is magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) compatible. It was prototyped to have four air-filled chambers that are made of silicone elastomer, which contains low magnetizing materials. When an individual compresses the device with his/her fingers, each chamber experiences a change in pressure, which is detected by a pressure sensor. In a previous recent work, our device was shown to be MEG/fMRI compatible. In this study, our research effort focuses on using the device to detect different finger actions (e.g., grasping and pinching) in non-shielded rooms. This is achieved by applying a support vector machine to the sensor data collected from the device when participants are resting and executing the different finger actions. The total number of possible finger actions that can be executed using the device is 31. The healthy participants could perform all the 31 different finger actions and the average classification accuracy achieved is 95.53 ± 2.63%. The stroke participants could perform all the 31 different finger actions with their healthy hand and the average classification accuracy achieved is 83.13 ± 6.69%. Unfortunately, the functions of their affected hands are compromised due to stroke. Thus, the number of finger actions they could perform ranges from 2 to 24, depending on the level of impairments. The average classification accuracy for the affected hand is 83.99 ± 16.38%. The ability to identify different finger actions using the device can provide a mean to researchers to label the data automatically in MEG/fMRI studies. In addition, the sensor data acquired from the device provide sensorimotor-­related information, such as speed and force, when the device is compressed. Thus, brain activations can be correlated with this information during different finger actions. Finally, the device can be used to assess the recovery of the sensory and motor functions of individuals with stroke when paired with fMRI. Frontiers Media S.A. 2016-01-11 /pmc/articles/PMC4707295/ /pubmed/26793701 http://dx.doi.org/10.3389/fbioe.2015.00205 Text en Copyright © 2016 Yong, Li and Menon. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Yong, Xinyi
Li, Yasong
Menon, Carlo
The Use of an MEG/fMRI-Compatible Finger Motion Sensor in Detecting Different Finger Actions
title The Use of an MEG/fMRI-Compatible Finger Motion Sensor in Detecting Different Finger Actions
title_full The Use of an MEG/fMRI-Compatible Finger Motion Sensor in Detecting Different Finger Actions
title_fullStr The Use of an MEG/fMRI-Compatible Finger Motion Sensor in Detecting Different Finger Actions
title_full_unstemmed The Use of an MEG/fMRI-Compatible Finger Motion Sensor in Detecting Different Finger Actions
title_short The Use of an MEG/fMRI-Compatible Finger Motion Sensor in Detecting Different Finger Actions
title_sort use of an meg/fmri-compatible finger motion sensor in detecting different finger actions
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4707295/
https://www.ncbi.nlm.nih.gov/pubmed/26793701
http://dx.doi.org/10.3389/fbioe.2015.00205
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