Clinical trial NCT05642299: Multimodal EEG and NIRS-based BCI With Assistive Soft Robotics for Stroke (MBCI-SR)
Official title: Multimodal EEG and NIRS-based BCI With Assistive Soft Robotics for Stroke
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Stroke
Keywords: Brain-Computer Interface; upper limb rehabilitation
Lead sponsor: Tan Tock Seng Hospital
Sponsor class: OTHER
Collaborators: name: Institute for Infocomm Research
name: Nanyang Technological University
First posted: 2022-12-08
Enrollment: 10
Sex: ALL
Minimum age (years): 50
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: TREATMENT

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.

Detailed description: 1. To address the "one-size-fits-all" stroke rehabilitation strategy, RRIS will develop an Ability data-driven personalized stroke rehabilitation based on the stroke patient's UL impairment and motor ability, by first matching 6 UL tasks in RRIS Ability Database with the 6 ADL tasks of the BCI-SR Intervention via similarity indices. A personalized subset of ADL tasks treatment options is then generated by a data-driven recommendation based on the patient's ability, movement pattern of the treatment option and the normative data from the RRIS Ability Database. A multi-modal BCI is proposed to perform EEG subject-specific calibration using Near-infrared spectroscopy, NIRS to ensure motor imagery compliance.
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.

Eligibility criteria: Inclusion Criteria:

* 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

Exclusion Criteria:

* 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

Primary outcomes:
measure: Fugl-Meyer score of upper limb
timeFrame: Baseline
measure: Fugl-Meyer score of upper limb
timeFrame: at week 4 (mid point)
measure: Fugl-Meyer score of upper limb
timeFrame: at week 6 (completion of intervention)
measure: Fugl-Meyer score of upper limb
timeFrame: at week 12 (at 3 month post intervention)
measure: Fugl-Meyer score of upper limb
timeFrame: at week 24 (at 6 month post intervention)
Secondary outcomes:
measure: Action Research Arm Test
timeFrame: Baseline
measure: Action Research Arm Test
timeFrame: at week 12 (at 3 months post intervention)
measure: Action Research Arm Test
timeFrame: at week 24 ( at 6 months post intervention)
Other outcomes:
Locations:
city: Singapore
country: Singapore
facility: Tan Tock Seng Hospital Rehabilitation Centre
Study officials:
affiliation: Tan Tock Seng Hospital
name: Chloe Lauha Chung, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Institute for Infocomm Research
name: Kai Keng Ang
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.