Multimodal EEG and NIRS-based BCI With Assistive Soft Robotics for Stroke (MBCI-SR)

NCT05642299 | Unknown

• 1 other identifier: 2021/00715
• Study Type: interventional
• Target: 10
• Locations: 1 country
• Sites: 1

Brief Summary

One-third of patients who had stroke suffered persistent disabilities, and upper limb (UL) motor impairment is one of the main disabilities. Recent clinical studies had been conducted using non-invasive EEG-based BCI via motor imagery, for post-stroke rehabilitation, yielded motor improvement of 7.2 on the Fugl-Meyer Motor Assessment (FMA-UE)score in chronic stroke patients that is significantly better than standard care. However, all the stroke patients underwent the same "one-size-fits-all" treatment option involving all six different activities of daily living (ADL)-oriented tasks regardless of their impairment or ability. 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.

Conditions

Stroke

Keywords

Brain-Computer Interface; upper limb rehabilitation

Outcome Measures

Primary Outcomes (5)

• Fugl-Meyer score of upper limb

  a measure for upper extremity, movement coordination and reflex action.

  Baseline

• Fugl-Meyer score of upper limb

  a measure for upper extremity, movement coordination and reflex action.

  at week 4 (mid point)

• Fugl-Meyer score of upper limb

  a measure for upper extremity, movement coordination and reflex action.

  at week 6 (completion of intervention)

• Fugl-Meyer score of upper limb

  a measure for upper extremity, movement coordination and reflex action.

  at week 12 (at 3 month post intervention)

• Fugl-Meyer score of upper limb

  a measure for upper extremity, movement coordination and reflex action.

  at week 24 (at 6 month post intervention)

Secondary Outcomes (3)

• Action Research Arm Test

  Baseline

• Action Research Arm Test

  at week 12 (at 3 months post intervention)

• Action Research Arm Test

  at week 24 ( at 6 months post intervention)

Study Arms (1)

MBCI-SR

EXPERIMENTAL

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.

Device: MBCI-SR

Interventions

MBCI-SR DEVICE

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.

MBCI-SR

Eligibility Criteria

• Age: 50 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• first ever stroke prior to clinical trial
• 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

You may not qualify if:

• recurrent stroke
• 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

Sponsors & Collaborators

• Tan Tock Seng Hospital: lead
• Institute for Infocomm Research: collaborator
• Nanyang Technological University: collaborator

Study Sites (1)

Tan Tock Seng Hospital Rehabilitation Centre

Singapore, 569766, Singapore

RECRUITING

Related Publications (5)

• 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.

  PMID: 18835541 BACKGROUND

• 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.

  PMID: 24756025 RESULT

• 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.

  PMID: 25120465 RESULT

• 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.

  PMID: 32248089 RESULT

• 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.

  PMID: 22208123 RESULT

MeSH Terms

Conditions

Stroke

Condition Hierarchy (Ancestors)

Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases

Study Officials

• Chloe Lauha Chung, PhD

  Tan Tock Seng Hospital

  PRINCIPAL INVESTIGATOR

• Kai Keng Ang

  Institute for Infocomm Research

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Chloe Lauha Chung, PhD

CONTACT

+65 6357 8305; chloe_lh_chung@ttsh.com.sg

Kai Keng Ang, PhD

CONTACT

+65 6408 2000; kkang@i2r.a-star.edu.sg

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Physiotherapist

Study Record Dates

• First Submitted: November 15, 2022
• First Posted: December 8, 2022
• Study Start: December 1, 2022
• Primary Completion: September 14, 2023
• Study Completion: December 14, 2023
• Last Updated: December 8, 2022

Record last verified: 2022-12

Data Sharing

• IPD Sharing: Will not share

Locations

SG (1)