Multimodal EEG and NIRS-based BCI With Assistive Soft Robotics for Stroke (MBCI-SR)
NCT ID: NCT05642299
Last Updated: 2022-12-08
Study Results
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Basic Information
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UNKNOWN
NA
10 participants
INTERVENTIONAL
2022-12-01
2023-12-14
Brief Summary
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Investigators hypothesize that precision personalized stroke rehabilitation intervention that is tailored to the patient hold more promise than a "one-size-fits-all" stroke rehabilitation strategy.
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Detailed Description
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2. stroke subjects with UL impairments (score 11-45 on the FMA-UE) will be recruited to undergo the UL tasks assessment at RRIS. They will then undergo the personalized stroke rehabilitation using the Multimodal EEG and NIRS-based BCI with Soft Robotic therapy for 1.5 hour over 6 weeks, 3 times a week. The effectiveness of the personalized stroke rehabilitation can then be retrospectively compared to the use of "one-size-fits-all" ADL tasks in the previous clinical trial.
Conditions
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Study Design
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NA
SINGLE_GROUP
TREATMENT
NONE
Study Groups
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MBCI-SR
BCI based robotic rehabilitation works by detecting the motor intent of the user from Electroencephalogram signals to drive rehabilitation assisted by the soft robotics gloves.
MBCI-SR
Participants will be asked to wear and EEG+NIRS cap and a soft robotic glove on their stroke-impaired hand. The participant will be instructed to ask to imagine to picture moving the stroke-imparied hand in the mind. The brain signal (EEG and NIRS data) will be recorded as a reference. When the participant pictures this move again, upon detection of such imagined move by MBCI-SR system, the glove will be activated and assists the participants to perform a specific upper limb task based on individual ability. There are six different activities of daily living (ADL)-oriented tasks enacted through a virtual arm and virtual objects, which formed the visual feedback for the participants. These tasks include scanning goods, moving an object upward to a cabinet, using two hands to move a towel, pouring of water into a cup, eating action and fine motor movement of picking up a small block using two fingers. Training intensity is 1.5 hours for 3 times a week for 6 weeks, a total of 18 sessions.
Interventions
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MBCI-SR
Participants will be asked to wear and EEG+NIRS cap and a soft robotic glove on their stroke-impaired hand. The participant will be instructed to ask to imagine to picture moving the stroke-imparied hand in the mind. The brain signal (EEG and NIRS data) will be recorded as a reference. When the participant pictures this move again, upon detection of such imagined move by MBCI-SR system, the glove will be activated and assists the participants to perform a specific upper limb task based on individual ability. There are six different activities of daily living (ADL)-oriented tasks enacted through a virtual arm and virtual objects, which formed the visual feedback for the participants. These tasks include scanning goods, moving an object upward to a cabinet, using two hands to move a towel, pouring of water into a cup, eating action and fine motor movement of picking up a small block using two fingers. Training intensity is 1.5 hours for 3 times a week for 6 weeks, a total of 18 sessions.
Eligibility Criteria
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Inclusion Criteria
* Fugl-Meyer Assessment scale of upper extremity impairment of 11-45 out of a maximum score of 66
* ability to give own consent
* ability to pay attention and maintain supported sitting for 1.5 hours continuously
* able to comprehend and follow commands
* fulfils BCI resting brain states on initial screening
* unilateral upper limb impairment
Exclusion Criteria
* inability to follow command and sit upright for 1.5 hours
* hemi-spatial neglect
* spasticity assessed by Modified Ashworth Scale more than 2/4
* History of Epilepsy
* Fixed contracture / deformity of finger joints
* upper limb pain impeding movements with visual analogy scale \> 4/10
* Severe aphasia or cognitive impairment despite visual aids
* other conditions ensuing upper limb weakness
* poor skin conditions
* skull defect that might affect EEG or NIRS reading
* allergy to electrodes or adhesive gel
* significant vision and hearing impairment affecting participation
* Pregnant women
50 Years
80 Years
ALL
No
Sponsors
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Institute for Infocomm Research
OTHER
Nanyang Technological University
OTHER
Tan Tock Seng Hospital
OTHER
Responsible Party
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Chloe Chung Lau Ha
Principal Physiotherapist
Principal Investigators
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Chloe Lauha Chung, PhD
Role: PRINCIPAL_INVESTIGATOR
Tan Tock Seng Hospital
Kai Keng Ang
Role: PRINCIPAL_INVESTIGATOR
Institute for Infocomm Research
Locations
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Tan Tock Seng Hospital Rehabilitation Centre
Singapore, , Singapore
Countries
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Central Contacts
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Facility Contacts
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References
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Daly JJ, Wolpaw JR. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 2008 Nov;7(11):1032-43. doi: 10.1016/S1474-4422(08)70223-0. Epub 2008 Oct 2.
Ang KK, Chua KS, Phua KS, Wang C, Chin ZY, Kuah CW, Low W, Guan C. A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke. Clin EEG Neurosci. 2015 Oct;46(4):310-20. doi: 10.1177/1550059414522229. Epub 2014 Apr 21.
Ang KK, Guan C, Phua KS, Wang C, Zhou L, Tang KY, Ephraim Joseph GJ, Kuah CW, Chua KS. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. Front Neuroeng. 2014 Jul 29;7:30. doi: 10.3389/fneng.2014.00030. eCollection 2014.
Cheng N, Phua KS, Lai HS, Tam PK, Tang KY, Cheng KK, Yeow RC, Ang KK, Guan C, Lim JH. Brain-Computer Interface-Based Soft Robotic Glove Rehabilitation for Stroke. IEEE Trans Biomed Eng. 2020 Dec;67(12):3339-3351. doi: 10.1109/TBME.2020.2984003. Epub 2020 Nov 19.
Ang KK, Guan C, Chua KS, Ang BT, Kuah CW, Wang C, Phua KS, Chin ZY, Zhang H. A large clinical study on the ability of stroke patients to use an EEG-based motor imagery brain-computer interface. Clin EEG Neurosci. 2011 Oct;42(4):253-8. doi: 10.1177/155005941104200411.
Other Identifiers
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2021/00715
Identifier Type: -
Identifier Source: org_study_id
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