Brain‐Computer Interfaces With Multi‐Sensory Feedback for Stroke Rehabilitation: A Case Study
Conventional therapies do not provide paralyzed patients with closed‐loop sensorimotor integration for motor rehabilitation. This work presents the recoveriX system, a hardware and software platform that combines a motor imagery (MI)‐based brain‐computer interface (BCI), functional electrical stimul...
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| Veröffentlicht in: | Artificial organs 2017-11, Vol.41 (11), p.E178-E184 |
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| container_title | Artificial organs |
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| creator | Irimia, Danut C. Cho, Woosang Ortner, Rupert Allison, Brendan Z. Ignat, Bogdan E. Edlinger, Guenter Guger, Christoph |
| description | Conventional therapies do not provide paralyzed patients with closed‐loop sensorimotor integration for motor rehabilitation. This work presents the recoveriX system, a hardware and software platform that combines a motor imagery (MI)‐based brain‐computer interface (BCI), functional electrical stimulation (FES), and visual feedback technologies for a complete sensorimotor closed‐loop therapy system for poststroke rehabilitation. The proposed system was tested on two chronic stroke patients in a clinical environment. The patients were instructed to imagine the movement of either the left or right hand in random order. During these two MI tasks, two types of feedback were provided: a bar extending to the left or right side of a monitor as visual feedback and passive hand opening stimulated from FES as proprioceptive feedback. Both types of feedback relied on the BCI classification result achieved using common spatial patterns and a linear discriminant analysis classifier. After 10 sessions of recoveriX training, one patient partially regained control of wrist extension in her paretic wrist and the other patient increased the range of middle finger movement by 1 cm. A controlled group study is planned with a new version of the recoveriX system, which will have several improvements. |
| doi_str_mv | 10.1111/aor.13054 |
| format | Article |
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This work presents the recoveriX system, a hardware and software platform that combines a motor imagery (MI)‐based brain‐computer interface (BCI), functional electrical stimulation (FES), and visual feedback technologies for a complete sensorimotor closed‐loop therapy system for poststroke rehabilitation. The proposed system was tested on two chronic stroke patients in a clinical environment. The patients were instructed to imagine the movement of either the left or right hand in random order. During these two MI tasks, two types of feedback were provided: a bar extending to the left or right side of a monitor as visual feedback and passive hand opening stimulated from FES as proprioceptive feedback. Both types of feedback relied on the BCI classification result achieved using common spatial patterns and a linear discriminant analysis classifier. After 10 sessions of recoveriX training, one patient partially regained control of wrist extension in her paretic wrist and the other patient increased the range of middle finger movement by 1 cm. A controlled group study is planned with a new version of the recoveriX system, which will have several improvements.</description><identifier>ISSN: 0160-564X</identifier><identifier>EISSN: 1525-1594</identifier><identifier>DOI: 10.1111/aor.13054</identifier><identifier>PMID: 29148137</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Adult ; Biomechanical Phenomena ; Brain ; Brain - physiopathology ; Brain Waves ; Brain-Computer Interfaces ; Brain‐computer interface ; Case studies ; Chronic Disease ; Computer applications ; Discriminant Analysis ; Electric Stimulation Therapy - instrumentation ; Electric Stimulation Therapy - methods ; Electrical stimuli ; Electroencephalography ; Equipment Design ; Feedback ; Feedback, Sensory ; Female ; Hand - innervation ; Human-computer interface ; Humans ; Imagery ; Implants ; Interfaces ; Linear Models ; Male ; Mental task performance ; Middle Aged ; Motor Activity ; Paralysis - diagnosis ; Paralysis - physiopathology ; Paralysis - rehabilitation ; Patients ; Pattern Recognition, Automated ; Proprioception ; Recovery of Function ; Rehabilitation ; Sensorimotor integration ; Sensory feedback ; Signal Processing, Computer-Assisted ; Stroke ; Stroke - diagnosis ; Stroke - physiopathology ; Stroke - therapy ; Stroke Rehabilitation - instrumentation ; Stroke Rehabilitation - methods ; stroke rehabilitation functional electrical stimulation neurofeedback ; Time Factors ; Treatment Outcome ; Visual perception ; Visual stimuli ; Wrist</subject><ispartof>Artificial organs, 2017-11, Vol.41 (11), p.E178-E184</ispartof><rights>2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3534-9af4984aeb73f1c93b409913e64d64b3af251043cab7c86765d3dab9939da5b63</citedby><cites>FETCH-LOGICAL-c3534-9af4984aeb73f1c93b409913e64d64b3af251043cab7c86765d3dab9939da5b63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Faor.13054$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Faor.13054$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29148137$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Irimia, Danut C.</creatorcontrib><creatorcontrib>Cho, Woosang</creatorcontrib><creatorcontrib>Ortner, Rupert</creatorcontrib><creatorcontrib>Allison, Brendan Z.</creatorcontrib><creatorcontrib>Ignat, Bogdan E.</creatorcontrib><creatorcontrib>Edlinger, Guenter</creatorcontrib><creatorcontrib>Guger, Christoph</creatorcontrib><title>Brain‐Computer Interfaces With Multi‐Sensory Feedback for Stroke Rehabilitation: A Case Study</title><title>Artificial organs</title><addtitle>Artif Organs</addtitle><description>Conventional therapies do not provide paralyzed patients with closed‐loop sensorimotor integration for motor rehabilitation. This work presents the recoveriX system, a hardware and software platform that combines a motor imagery (MI)‐based brain‐computer interface (BCI), functional electrical stimulation (FES), and visual feedback technologies for a complete sensorimotor closed‐loop therapy system for poststroke rehabilitation. The proposed system was tested on two chronic stroke patients in a clinical environment. The patients were instructed to imagine the movement of either the left or right hand in random order. During these two MI tasks, two types of feedback were provided: a bar extending to the left or right side of a monitor as visual feedback and passive hand opening stimulated from FES as proprioceptive feedback. Both types of feedback relied on the BCI classification result achieved using common spatial patterns and a linear discriminant analysis classifier. After 10 sessions of recoveriX training, one patient partially regained control of wrist extension in her paretic wrist and the other patient increased the range of middle finger movement by 1 cm. A controlled group study is planned with a new version of the recoveriX system, which will have several improvements.</description><subject>Adult</subject><subject>Biomechanical Phenomena</subject><subject>Brain</subject><subject>Brain - physiopathology</subject><subject>Brain Waves</subject><subject>Brain-Computer Interfaces</subject><subject>Brain‐computer interface</subject><subject>Case studies</subject><subject>Chronic Disease</subject><subject>Computer applications</subject><subject>Discriminant Analysis</subject><subject>Electric Stimulation Therapy - instrumentation</subject><subject>Electric Stimulation Therapy - methods</subject><subject>Electrical stimuli</subject><subject>Electroencephalography</subject><subject>Equipment Design</subject><subject>Feedback</subject><subject>Feedback, Sensory</subject><subject>Female</subject><subject>Hand - innervation</subject><subject>Human-computer interface</subject><subject>Humans</subject><subject>Imagery</subject><subject>Implants</subject><subject>Interfaces</subject><subject>Linear Models</subject><subject>Male</subject><subject>Mental task performance</subject><subject>Middle Aged</subject><subject>Motor Activity</subject><subject>Paralysis - diagnosis</subject><subject>Paralysis - physiopathology</subject><subject>Paralysis - rehabilitation</subject><subject>Patients</subject><subject>Pattern Recognition, Automated</subject><subject>Proprioception</subject><subject>Recovery of Function</subject><subject>Rehabilitation</subject><subject>Sensorimotor integration</subject><subject>Sensory feedback</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Stroke</subject><subject>Stroke - diagnosis</subject><subject>Stroke - physiopathology</subject><subject>Stroke - therapy</subject><subject>Stroke Rehabilitation - instrumentation</subject><subject>Stroke Rehabilitation - methods</subject><subject>stroke rehabilitation functional electrical stimulation neurofeedback</subject><subject>Time Factors</subject><subject>Treatment Outcome</subject><subject>Visual perception</subject><subject>Visual stimuli</subject><subject>Wrist</subject><issn>0160-564X</issn><issn>1525-1594</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp10MtO3DAUBmCrKioDdNEXqCx1UxYBO74k7m464iZRjTRQtbvoODkRhkw82InQ7HgEnpEnwTDQBVK9OF6cT7-OfkK-cHbA0zsEHw64YEp-IBOucpVxZeRHMmFcs0xp-Xeb7MR4zRgrJNOfyHZuuCy5KCYEfgZw_eP9w8wvV-OAgZ71abZQY6R_3HBFf43d4BK4wD76sKbHiI2F-oa2PtCLIfgbpAu8Aus6N8DgfP-DTukMIqbt2Kz3yFYLXcTPr_8u-X18dDk7zc7nJ2ez6XlWCyVkZqCVppSAthAtr42wkhnDBWrZaGkFtLniTIoabFGXutCqEQ1YY4RpQFktdsn3Te4q-NsR41AtXayx66BHP8aKG61zIY0sE_32jl77MfTpumelWJ6zUia1v1F18DEGbKtVcEsI64qz6rn3KvVevfSe7NfXxNEusfkn34pO4HAD7lyH6_8nVdP5YhP5BIhxjUs</recordid><startdate>201711</startdate><enddate>201711</enddate><creator>Irimia, Danut C.</creator><creator>Cho, Woosang</creator><creator>Ortner, Rupert</creator><creator>Allison, Brendan Z.</creator><creator>Ignat, Bogdan E.</creator><creator>Edlinger, Guenter</creator><creator>Guger, Christoph</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>201711</creationdate><title>Brain‐Computer Interfaces With Multi‐Sensory Feedback for Stroke Rehabilitation: A Case Study</title><author>Irimia, Danut C. ; Cho, Woosang ; Ortner, Rupert ; Allison, Brendan Z. ; Ignat, Bogdan E. ; Edlinger, Guenter ; Guger, Christoph</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3534-9af4984aeb73f1c93b409913e64d64b3af251043cab7c86765d3dab9939da5b63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adult</topic><topic>Biomechanical Phenomena</topic><topic>Brain</topic><topic>Brain - physiopathology</topic><topic>Brain Waves</topic><topic>Brain-Computer Interfaces</topic><topic>Brain‐computer interface</topic><topic>Case studies</topic><topic>Chronic Disease</topic><topic>Computer applications</topic><topic>Discriminant Analysis</topic><topic>Electric Stimulation Therapy - instrumentation</topic><topic>Electric Stimulation Therapy - methods</topic><topic>Electrical stimuli</topic><topic>Electroencephalography</topic><topic>Equipment Design</topic><topic>Feedback</topic><topic>Feedback, Sensory</topic><topic>Female</topic><topic>Hand - innervation</topic><topic>Human-computer interface</topic><topic>Humans</topic><topic>Imagery</topic><topic>Implants</topic><topic>Interfaces</topic><topic>Linear Models</topic><topic>Male</topic><topic>Mental task performance</topic><topic>Middle Aged</topic><topic>Motor Activity</topic><topic>Paralysis - diagnosis</topic><topic>Paralysis - physiopathology</topic><topic>Paralysis - rehabilitation</topic><topic>Patients</topic><topic>Pattern Recognition, Automated</topic><topic>Proprioception</topic><topic>Recovery of Function</topic><topic>Rehabilitation</topic><topic>Sensorimotor integration</topic><topic>Sensory feedback</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Stroke</topic><topic>Stroke - diagnosis</topic><topic>Stroke - physiopathology</topic><topic>Stroke - therapy</topic><topic>Stroke Rehabilitation - instrumentation</topic><topic>Stroke Rehabilitation - methods</topic><topic>stroke rehabilitation functional electrical stimulation neurofeedback</topic><topic>Time Factors</topic><topic>Treatment Outcome</topic><topic>Visual perception</topic><topic>Visual stimuli</topic><topic>Wrist</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Irimia, Danut C.</creatorcontrib><creatorcontrib>Cho, Woosang</creatorcontrib><creatorcontrib>Ortner, Rupert</creatorcontrib><creatorcontrib>Allison, Brendan Z.</creatorcontrib><creatorcontrib>Ignat, Bogdan E.</creatorcontrib><creatorcontrib>Edlinger, Guenter</creatorcontrib><creatorcontrib>Guger, Christoph</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Artificial organs</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Irimia, Danut C.</au><au>Cho, Woosang</au><au>Ortner, Rupert</au><au>Allison, Brendan Z.</au><au>Ignat, Bogdan E.</au><au>Edlinger, Guenter</au><au>Guger, Christoph</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Brain‐Computer Interfaces With Multi‐Sensory Feedback for Stroke Rehabilitation: A Case Study</atitle><jtitle>Artificial organs</jtitle><addtitle>Artif Organs</addtitle><date>2017-11</date><risdate>2017</risdate><volume>41</volume><issue>11</issue><spage>E178</spage><epage>E184</epage><pages>E178-E184</pages><issn>0160-564X</issn><eissn>1525-1594</eissn><abstract>Conventional therapies do not provide paralyzed patients with closed‐loop sensorimotor integration for motor rehabilitation. This work presents the recoveriX system, a hardware and software platform that combines a motor imagery (MI)‐based brain‐computer interface (BCI), functional electrical stimulation (FES), and visual feedback technologies for a complete sensorimotor closed‐loop therapy system for poststroke rehabilitation. The proposed system was tested on two chronic stroke patients in a clinical environment. The patients were instructed to imagine the movement of either the left or right hand in random order. During these two MI tasks, two types of feedback were provided: a bar extending to the left or right side of a monitor as visual feedback and passive hand opening stimulated from FES as proprioceptive feedback. Both types of feedback relied on the BCI classification result achieved using common spatial patterns and a linear discriminant analysis classifier. After 10 sessions of recoveriX training, one patient partially regained control of wrist extension in her paretic wrist and the other patient increased the range of middle finger movement by 1 cm. A controlled group study is planned with a new version of the recoveriX system, which will have several improvements.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>29148137</pmid><doi>10.1111/aor.13054</doi><tpages>7</tpages></addata></record> |
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| subjects | Adult Biomechanical Phenomena Brain Brain - physiopathology Brain Waves Brain-Computer Interfaces Brain‐computer interface Case studies Chronic Disease Computer applications Discriminant Analysis Electric Stimulation Therapy - instrumentation Electric Stimulation Therapy - methods Electrical stimuli Electroencephalography Equipment Design Feedback Feedback, Sensory Female Hand - innervation Human-computer interface Humans Imagery Implants Interfaces Linear Models Male Mental task performance Middle Aged Motor Activity Paralysis - diagnosis Paralysis - physiopathology Paralysis - rehabilitation Patients Pattern Recognition, Automated Proprioception Recovery of Function Rehabilitation Sensorimotor integration Sensory feedback Signal Processing, Computer-Assisted Stroke Stroke - diagnosis Stroke - physiopathology Stroke - therapy Stroke Rehabilitation - instrumentation Stroke Rehabilitation - methods stroke rehabilitation functional electrical stimulation neurofeedback Time Factors Treatment Outcome Visual perception Visual stimuli Wrist |
| title | Brain‐Computer Interfaces With Multi‐Sensory Feedback for Stroke Rehabilitation: A Case Study |
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