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Table 2 Summary of studies

From: Brain–computer interface robotics for hand rehabilitation after stroke: a systematic review

Authors

Participants

Study design

Task design

BCI-Hand robot

Main outcomes

Studies involving stroke patients

   

 Ang et al. 2014 [75]

N = 27 (7F:14 M) Moderate to severe impairment of UE function Mean age: 54.2y Mean stroke duration: 385.1 days

3-armed RCT of motor function with MI-BCI-device as intervention

Control groups: device only (Haptic Knob), SAT

Photo manipulation: hand opening and closing, pronation and supination

Cue: visual (photo)

Feedback: visual (photo) and kinaesthetic

EEG: 27 channels to classify ERD/ERS and coupled with EMG to confirm MI

Device: Haptic Knob, 2-DOF for hand grasping and knob manipulation

Actuation: DC brushed motors with linear belt drive

Control: trigger

Clinical outcome measure: FMMA Distal, improvement in weeks 3, 6, 12, 24

BCI-device group = 2.5 ± 2.4, 3.3 ± 2.3, 3.2 ± 2.7, 4.2 ± 3.1

Device only group = 1.6 ± 2.5, 2.9 ± 3.0, 2.5 ± 2.6, 2.5 ± 3.0

SAT group = 0.4 ± 1.1, 1.9 ± 1.9, 1.0 ± 1.3, 0.3 ± 2.1

 Barsotti et al. 2015 [76]

N = 3 (1F:2 M) Chronic stroke survivors with right arm hemiparesis Mean age: 62 ± 12y

Probing MI classification by BCI training, time–frequency analysis and robot trajectories

Uncontrolled

Reaching-grasping-releasing

Cue: visual

Feedback: kinaesthetic

Minimum time required to perform MI = 2 s

EEG: 13 channels to classify ERD

Device: BRAVO 2-DOF hand orthosis attached to full UE exoskeleton

Actuation: DC motors with rigid links

Control: trigger

Mean classification accuracy during BCI training = 82.51 ± 2.04%

Average delay from visual cue to robot initiation = 3.45 ± 1.6 s

Average delay due to patient’s ability to start MI = 1.45 s

 Bundy et al. 2017 [77]

N = 10 Chronic hemiparetic stroke with moderate to severe UE hemiparesis Mean age: 58.6 ± 10.3y

Motor function evaluation before and after intervention by MI-BCI from unaffected hemisphere

Uncontrolled

Opening of affected hand

Cue: visual

Feedback: visual and kinaesthetic

EEG: 8 channels to classify ERD

Device: 3-pinch grip, 1-DOF hand exoskeleton

Control: continuous depending on spectral power

Clinical outcome measure: ARAT Score, improvement from baseline to completion (12 weeks)

Mean ± SD = 6.20 ± 3.81

Note: 5.7 ARAT Score is the minimal clinically important difference in chronic stroke survivors

 Carino-Escobar et al. 2019 [85]

N = 9 (4F:5 M) Subacute ischaemic stroke Mean age: 59.9 ± 2.8y Mean stroke duration: 158(± 74)-185(± 73) days

Determine longitudinal ERD/ERS patters and functional recovery with BCI-robot

Uncontrolled

Extension-flexion of hand fingers

Cue: visual (Graz MI)

Feedback: visual and kinaesthetic

EEG: 11 channels to classify ERD/ERS

Device: hand finger orthosis

Actuation: DC motor with screw system for linear displacement, flexible links

Control: trigger

FMA-UE: N = 3 reported equal or higher than 3 score gains, N = 3 no score gains,

Mean longitudinal ERD/ERS: beta bands have higher association with time since stroke onset than alpha, and strong association with UL motor recovery

 Chowdhury et al. 2018-b [78]

N = 20 10 healthy and 10 hemiplegic stroke patients Mean age (healthy, stroke): 41 ± 9.21y, 47.5 ± 14.23y

Probe non-adaptive classifier (NAC) vs. Covariate Shift adaptive classifier (CSAC) of MI in EEG

Control group: healthy participants

Extension-flexion of hand fingers

Cue: visual

Feedback: visual and kinaesthetic

EEG: 12 channels with EMG to classify ERD/ERS

Device: EMOHEX 3-finger, 3-DOF each, exoskeleton (thumb, index, middle)

Actuation: servomotors with rigid links

Control: trigger

Mean classification accuracies during BCI training:

Healthy group: calibration = 78.50 ± 9.01%, NAC = 75.25 ± 5.46%, CSAC = 81.50 ± 4.89%

Patient group: calibration = 79.63 ± 13.11%, NAC = 70.25 ± 3.43%, CSAC = 75.75 ± 3.92%

 Chowdhury et al., 2018-c [79]

N = 4 (2F:2 M)

Chronic hemiplegic stroke patients, right-handed, left hand impaired Mean age: 44.75 ± 15.69y Mean stroke duration: 7 ± 1.15mo

Motor function evaluation by using active physical practice followed by MI-BCI-controlled device intervention

Uncontrolled

Extension-flexion of hand fingers

Cue: visual

Feedback: visual and kinaesthetic

EEG: 12 channels with force sensors to classify ERD/ERS

Device: EMOHEX 3-finger, 3-DOF each, exoskeleton (thumb, index, middle)

Actuation: servomotors with rigid links

Control: trigger

Classification accuracies of 4 participants: P01 = 81.45 ± 8.12%, P02 = 70.21 ± 4.43%, P03 = 76.88 ± 4.49%, P04 = 74.55 ± 4.35%

Clinical outcome measures: GS and ARAT Scores, improvement from baseline to completion (6 weeks)

GS scores: group mean difference =  + 6.38 kg, p = 0.06

ARAT scores: group mean difference =  + 5.66, p < 0.05

 Frolov et al., 2017 [80]

N = 74 (26F:48 M) BCI 55: Control 19 Subacute or chronic stroke with mild to hemiplegic hand paresis, right-handed

Multi-centre RCT of MI-BCI-controlled hand exoskeleton

Control group: SHAM

3 Tasks: (1) motor relaxation, (2) imagery of left-hand opening, (3) imagery of right-hand opening

Cue: visual

Feedback: visual and kinaesthetic

EEG: 30 channels to classify the three mental tasks by Bayesian classifier based on covariance matrices

Device: hand exoskeleton by Neurobotics, Russia

Actuation: pneumatic motors with spring flexors

Control: trigger

Mean classification accuracy during BCI training = 40.6%

Clinical outcome measures:

FMMA Distal and ARAT Scores, improvement in 10 days of training

FMMA Distal = 2.0, p < 0.01 (BCI) and 1.0, p = 0.046 (control)

ARAT Grasp = 3.0, p < 0.01 (BCI) and 1.0, p = 0.0394 (control)

ARAT Grip = 1.0, p < 0.01 (BCI) and 1.0, p = 0.045 (control)

ARAT Pinch = 1.0, p < 0.01 (BCI) and 0.0, p = 0.675 (control)

 Norman et al., 2018 [82]

N = 8 (All male) Chronic cortical and subcortical single haemorrhagic or ischaemic stroke (at least 6 months) Mean age: 59.5 ± 11.8y

Implementation of sensorimotor rhythm (SMR) control on robot-assistive movement

Uncontrolled

Extension of hand finger

Cue: visual

Feedback: visual and kinaesthetic

EEG: 16 channels mapping SMR changes

Device: FINGER robot

Actuation: Linear servo-tube actuator with rigid links

Control: Visual—continuous (colour change respective to SMR), Robot—trigger

Mean classification accuracies:

8 participants: 83.1%, 76.3%, 73.3%, 68.2%, 74.5%, 86.5%, 47.9%, 40.0%

Box and blocks test (BBT):

At screening: mean score = 14.3 ± 10.0, mean change after therapy = 4.3 ± 4.5 (range 0–12). Higher score changes in participants who demonstrated SMR control but not significant (p = 0.199)

 Ono et al., 2016-a [81]

N = 21 (9F:12 M) Chronic stroke patients with hemiplegic hands Mean age: 57.9 ± 2.4y

Probe congruent vs. incongruent MI feedback strategies

Control groups: congruent (synchronous proprioceptive and visual feedback) and incongruent (proprioceptive feedback given 1 s after visual)

Grasping of a tennis ball with a hand

Cue: visual (video of hand performing action)

Feedback: visual and kinaesthetic

EEG: 9 channels to classify ERD

Device: Power Assist Hand—Team ATOM, Atsugi, Japan

Actuation: pneumatic motors with rigid links

Control: trigger

Mean classification accuracies:

Congruent feedback = 56.8 ± 5.2%, chance level = 36.4 ± 4.5%

Incongruent feedback = 40.0 ± 3.5%, chance level 35.4 ± 4.5%

 Tsuchimoto et al. 2019 [84]

N = 18 (3F:14 M) Chronic haemorrhagic or ischaemic stroke (from 2mo onwards) Mean age: 58 ± 10y

Implementation of MI-controlled robotic orthosis as neurofeedback

Control: SHAM

Extension of hand finger

Cue: unspecified

Feedback: kinaesthetic and electrical stimulation

EEG: 5 channels to classify MI

Device: robotic finger orthosis

Actuation: servo motors with rigid links

Control: trigger

Significant time-intervention interaction in the ipsilesional sensorimotor cortex. Higher coactivation of sensory and motor cortices for neurofeedback group in the ipsilesional sensorimotor cortices as compared to SHAM

 Wang et al. 2018 [83]

N = 24 (4F:20 M) Chronic stroke patients with paralysed hands Mean age: 54 ± 9y

Implementation of action observation and motor imagery (AO + MI) with kinaesthetic feedback

Control: SHAM

Hand grasping

Cue: visual (video of hand action / textual cues in SHAM group)

Feedback: visual and kinaesthetic

EEG: 16 channels to classify ERD

Device: robot hand

Control: Trigger

AO + MI with kinaesthetic feedback group showed significant improvements in FMA-UE across longitudinal evaluation [χ2(2) = 7.659, p = 0.022], no significant difference in SHAM group [χ2(2) = 4.537, p = 0.103]

Studies involving healthy participants

   

 Bauer et al. 2015 [97]

N = 20 (11F:9 M) Right-handed Mean age: 28.5 ± 10.5y

Study on MI as compared to motor execution (ME) using BCI-device

Opening of left hand

Cue: auditory

Feedback: kinaesthetic

EEG: 31 channels to detect ERD, with EMG to classify MI from execution and account for tonic contraction

Device: Amadeo, Tyromotion, Austria

Control: discontinuation of ERD stops finger extension

Principal component analyses (between MI and execution) generated coefficients for the visual (VIS) and kinaesthetic (KIS) imagery scale, BCI-robot performance (BRI), tonic contraction task (MOC) and visuomotor integration task (VMI). VIS and KIS yielded high coefficients on MI while MOC and VMI yield high coefficients on ME. BRI show high coefficient yields on both MI and ME

 Cantillo-Negrete et al. 2015 [86]

N = 1

Design and implementation of a MI-controlled hand orthosis

Extension-flexion of right-hand finger

Cue: visual (modified Graz)

Feedback: kinaesthetic

EEG: 11 channels to detect MI

Device: 1-DOF hand finger orthosis

Actuation: DC motor with screw system for linear displacement, flexible links

Control: trigger

Classification accuracy = 78%

 Chowdhury et al., 2015-a [87]

N = 6 Age range: 20-30y

Study of cortico-muscular coupling in robotic finger exoskeleton control

Extension-flexion of hand fingers

Cue: visual

Feedback: kinaesthetic

EEG: 10 channels with EMG to classify MI

Device: 3-finger, 3-DOF each, exoskeleton (thumb, index, middle)

Actuation: servomotors with rigid links

Control: trigger

Mean classification accuracies: passive execution = 69.17%, hand execution = 71.25%, MI = 67.92%

 Coffey et al. 2014 [92]

N = 3 (All male) Right-handed

Age range: 24-28y

Design and implementation of a MI-controlled hand orthosis

Hand digit and wrist contraction and extension

Cue: visual (Graz MI)

Feedback: kinaesthetic

EEG: 27 channels to classify MI

Device: hand glove controlled by Arduino

Actuation: pneumatic

Control: trigger

Glove inflation-deflation cycle = 22 s

Classification accuracies of 3 participants: A = 92.5%, B = 90.0%, C = 80.0%

 Diab et al. 2016 [103]

N = 5

Design and implementation of EEG-triggered wrist orthosis with accuracy improvement

Hand opening and closing

Cue: verbal instruction

Feedback: kinaesthetic

EEG: 14 channels to detect hand movement-related EEG

Device: actuated Talon wrist orthosis

Actuation: linear

Control: trigger

Mean classification accuracies: simulation studies = 95%, online BCI training = 86%

 Fok et al. 2011 [102]

N = 4

Design and implementation of a MI-controlled hand orthosis

Hand opening and closing

Cue: unspecified

Feedback: visual (cursor movement) and kinaesthetic

EEG: 14 channels to detect MI-related ERD

Device: actuated Talon wrist orthosis

Actuation: linear actuator

Control: trigger

EEG signals from imagined hand movement was correlated with the contralesional hemisphere and utilised to trigger the actuation of orthosis

ERD was detected from 12 Hz bin power of EEG during move condition

 Li et al. 2019 [88]

N = 14 (4F:10 M) Mean age: 23.8 ± 0.89y

Design and implementation of an attention-controlled hand exoskeleton with rigid-soft mechanism

Hand grasping

Cue: visual (video of hand action)

Feedback: kinaesthetic

EEG: 3 channels to map signals relative to attention

Device: hand exoskeleton

Actuation: linear actuator with rigid-soft mechanism

Control: Trigger

Mean classification accuracy:

95.54% actuation success rate against the attention threshold

 Holmes et al. 2012 [93]

N = 6 (All male, young adults)

Design and implementation of a MI-controlled hand orthosis

Hand opening and closing

Cue: textual

Feedback: kinaesthetic

EEG: 14 channels to detect hand movement-related EEG

Device: ExoFlex Hand Exoskeleton controlled by Arduino

Actuation: linear actuator connected to chained links that flex

Control: trigger

Classification accuracies of 6 participants: T001 = 95%, T002 = 98%, D001 = 91%, U001 = 93%, E001 = 87%, E002 = 86%

 King et al. 2011 [104]

N = 1 (Female) 24y

Contralateral control of hand orthosis using EEG-based BCI

Right hand idling and grasping

Cue: textual

Feedback: visual and kinaesthetic

EEG: 63 channels to control contralateral hand movement

Device: hand orthosis

Actuation: servomotors attached to Bowden cables as tendons

Control: trigger

Offline classification accuracy = 95.3 ± 0.6%, p < 3.0866 × 10−25

Average lag from voluntary contractions to BCI-robot control = 2.24 ± 0.19 s (after 5 sessions)

 Naros et al. 2016 [98]

N = 32 (16F:16 M) Mean age: 25.9 ± 0.5y

2 × 2 factorial design with parameters: adaptive classifier threshold and non-adaptive classifier threshold, contingent feedback and non-contingent feedback

Opening of right hand

Cue: auditory

Feedback: kinaesthetic

EEG: 32 channels to detect ERD, with EMG to classify MI (FC3, C3, CP3 used)

Device: Amadeo, Tyromotion, Austria

Control: trigger

Significant enhancement in group 1 (adaptive classifier + contingent feedback), p = 0.0078

Significant reduction in group 4 (non-adaptive classifier + non-contingent feedback), p = 0.0391

Motor performance improvement over baseline from first and last tasks, significant results:

Group 1 (adaptive classifier + contingent feedback), p = 0.0313

Group 4 = (non-adaptive classifier + non-contingent feedback), p = 0.0411

 Ono et al. 2018-b [100]

N = 28 Right-handed except 1

Implementation of an action observation strategy with visual and proprioceptive, or auditory feedback to MI

Control group: SHAM

Grasping of a tennis ball with a hand

Cue: visual (video of hand performing action)

Feedback: visual, kinaesthetic and auditory

EEG: 9 channels to classify ERD

Device: Power Assist Hand—Team ATOM, Atsugi, Japan

Actuation: pneumatic motors with rigid links

Control: trigger

AO + MI + proprioceptive and visual feedback:

Mean MI-ERD powers of correct feedback vs SHAM provide significant interaction, F1,17 = 6.618, p = 0.020 (6 days)

Statistically significant increase in MI-ERD power in correct feedback group over baseline, p = 0.012 (6 days)

 Stan et al. 2015 [94]

N = 9

Trigger a hand orthosis using a P300 speller BCI

Spell E (enable), A (hand opening) and B (hand closing) in P300 speller BCI to perform hang grasping, moving and releasing objects

Cue: textual (spelling)

Feedback: visual (textual) and kinaesthetic

EEG: 8 channels focusing on visual cortex

Device: hand orthosis

Actuation: 2 servomotors and current feedback circuitry

Control: trigger

Mean classification accuracies: 100% (on 6th letter flash during calibration)

 Ramos-Murguialday et al. 2012 [95]

N = 23 Mean age (contingent positive, contingent negative, SHAM): 26.6 ± 4y, 26.5 ± 5y, 26.2 ± 2y

Probing MI with proprioceptive feedback

Experimental groups: contingent positive, contingent negative feedback

Control group: SHAM

5 tasks: MI without direct control, MI with direct control, passive, active, rest

Cue: auditory

Feedback: visual and kinaesthetic

EEG: 61 channels with EMG to classify ERD/ERS

Device: hand orthosis

Actuation: DC motor M-28 with a worm gearhead and Bowden cables for each finger

Control: trigger

Contingent positive feedback provided higher BCI performance during MI without feedback than contingent negative and SHAM; and higher during MI with or without feedback as compared to rest

 Ramos-Murguialday and Birbaumer 2015 [96]

N = 9 Right-handed Mean age: 26.6 ± 4y

Detect oscillatory signatures of motor tasks during EEG

5 tasks: MI without direct control, MI with direct control, passive, active, rest

Cue: auditory

Feedback: visual and kinaesthetic

EEG: 61 channels with EMG to classify ERD/ERS

Device: hand orthosis

Actuation: DC motor M-28 with a worm gearhead and Bowden cables for each finger

Control: trigger

Significant change in power in all frequency ranges during MI with direct control before trial initiation

Kinaesthetic feedback increased significant changes in alpha and beta power; therefore, increasing BCI performance

 Randazzo et al. 2018 [90]

N = 9 (2F:7 M) Mean age: 23 ± 5y

Design and implementation of a hand orthosis with testing of kinaesthetic effects in EEG

4 tasks: rest (REST), exoskeleton-induced hand motions (EXO), MI of right hand (MI), exoskeleton-induced hand motions plus MI (MIEXO)

Cue: visual

Feedback: kinaesthetic

EEG: 16 channels to detect MI

Device: mano hand exoskeleton

Actuation: linear servomotors attached to Bowden cables as tendons

Control: passive (exoskeleton not dependent on MI to move during MIEXO task)

Mean classification accuracies among groups:

(vs REST) MI = 63.02 ± 5.91%, EXO = 69.64 ± 5.74%, MIEXO = 72.19 ± 6.57%

MIEXO vs EXO = 69.91 ± 9.86%

Chance level at 95% confidence = 58% (N = 50 trials)

 Tacchino et al. 2017 [91]

N = 8 (7F:1 M) Right-handed Mean age: 26.3 ± 1.9y

2 × 2 factorial design with parameters: glove, no glove, active movement, passive movement

Opening and closing of hand, 4 tasks: (A) glove with active movement, (B) glove with passive movement, (C) no glove with active movement, (D) no glove and no movement

Cue: auditory

Feedback: kinaesthetic

EEG: 19 channels with EMG to detect ERD/ERS (C3, F3, Cz used)

Device: Gloreha hand rehabilitation glove

Actuation: electric actuators with Bowden cables on each finger

Control: passive (glove not dependent on brain-state during tasks)

Statistically significant ERD changes in beta and mu bands were observed to initiate earlier in tasks A and C (involves active movement)

Stronger and longer ERD was observed in tasks A and B (involves robotic assistance) suggesting reinforced afferent kinaesthetic feedback

 Vukelic and Gharabaghi 2015 [99]

N = 11 (4F:7 M) Right-handed Mean age: 25.83 ± 3.1y

Assessment sensorimotor activity during MI with either visual or kinaesthetic feedback

Right hand opening

Cue: visual (coloured cursor ball)

Feedback: visual and kinaesthetic (separated by experimental groups)

EEG: 128 channels to detect ERD/ERS during MI (F3, CP3, C3 used)

Device: Amadeo, Tyromotion, Austria

Control: trigger

MI + kinaesthetic feedback group resulted in higher beta ERS (p = 0.02) during rest and higher beta ERD (p = 0.04) during MI

Kinaesthetic feedback provides higher stability and sustained beta ERD activity than visual feedback

 Witkowski et al. 2014 [101]

N = 12 (4F:8 M) Right-handed Mean age: 28.1 ± 3.63y

Assessment performance and safety of EEG-EOG hybrid BCI

Right hand grasping

Cue: visual (coloured squares and arrows)

Feedback: kinaesthetic

EEG: 5 channels with EOG and EMG to detect ERD during MI

Device: HX hand exoskeleton

Actuation: DC motors with Bowden cables for thumb and index fingers

Control: trigger

Mean classification accuracies:

EEG only = 63.59 ± 10.81%

EEG/EOG hybrid = 60.77 ± 9.42%

Mean safety criterion violations during rest:

EEG only = 45.91 ± 26.8%

EEG/EOG hybrid = 10.14 ± 0.3%

 Zhang et al. 2019 [89]

N = 6 (2F:4 M) Right-handed Age range: 23-26y

Implementation of a multimodal system using EEG, EMG and EOG to control a soft-robotic hand

Graz visualisation and auditory instructions, eye movements and physical practice (hand gestures)

Cue: visual (Graz MI), auditory

Feedback: visual and kinaesthetic

EEG with EMG and EOG: 40 channels to analyse ERD/ERS patterns

Device: Soft pneumatic finger

Actuation: pneumatic actuator with soft structures

Control: trigger

Mean classification accuracies:

EOG = 94.23%

EEG = 31.46%

EMG = 36.38%

Multimodal = 93.83 ± 0.02%