Karen Sui Geok Chua

A Large Clinical Study on the Ability of Stroke Patients to Use an EEG-Based Motor Imagery Brain-Computer Interface

Brain-computer interface (BCI) technology has the prospects of helping stroke survivors by enabling the interaction with their environment through brain signals rather than through muscles, and restoring motor function by inducing activity-dependent brain plasticity. This paper presents a clinical study on the extent of detectable brain signals from a large population of stroke patients in using EEG-based motor imagery BCI. EEG data were collected from 54 stroke patients whereby finger tapping and motor imagery of the stroke-affected hand were performed by 8 and 46 patients, respectively. EEG data from 11 patients who gave further consent to perform motor imagery were also collected for second calibration and third independent test sessions conducted on separate days. Off-line accuracies of classifying the two classes of EEG from finger tapping or motor imagery of the stroke-affected hand versus the EEG from background rest were then assessed and compared to 16 healthy subjects. The mean off-line accuracy of detecting motor imagery by the 46 patients (μ=0.74) was significantly lower than finger tapping by 8 patients (μ=0.87, p=0.008), but not significantly lower than motor imagery by healthy subjects (μ=0.78, p=0.23). Six stroke patients performed motor imagery at chance level, and no correlation was found between the accuracies of detecting motor imagery and their motor impairment in terms of Fugl-Meyer Assessment (p=0.29). The off-line accuracies of the 11 patients in the second session (μ=0.76) were not significantly different from the first session (μ=0.72, p=0.16), or from the on-line accuracies of the third independent test session (μ=0.82, p=0.14). Hence this study showed that the majority of stroke patients could use EEG-based motor imagery BCI.

Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback

This clinical study investigates the ability of hemiparetic stroke patients in operating EEG-based motor imagery brain-computer interface (MI-BCI). It also assesses the efficacy in motor improvements on the stroke-affected upper limb using EEG-based MI-BCI with robotic feedback neurorehabilitation compared to robotic rehabilitation that delivers movement therapy. 54 hemiparetic stroke patients with mean age of 51.8 and baseline Fugl-Meyer Assessment (FMA) 14.9 (out of 66, higher = better) were recruited. Results showed that 48 subjects (89%) operated EEG-based MI-BCI better than at chance level, and their ability to operate EEG-based MI-BCI is not correlated to their baseline FMA (r=0.358). Those subjects who gave consent are randomly assigned to each group (N=11 and 14) for 12 1-hour rehabilitation sessions for 4 weeks. Significant gains in FMA scores were observed in both groups at post-rehabilitation (4.5, 6.2; p=0.032, 0.003) and 2-month post-rehabilitation (5.3, 7.3; p=0.020, 0.013), but no significant differences were observed between groups (p=0.512, 0.550). Hence, this study showed evidences that a majority of hemiparetic stroke patients can operate EEG-based MI-BCI, and that EEG-based MI-BCI with robotic feedback neurorehabilitation is effective in restoring upper extremities motor function in stroke.

A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke

Electroencephalography (EEG)–based motor imagery (MI) brain-computer interface (BCI) technology has the potential to restore motor function by inducing activity-dependent brain plasticity. The purpose of this study was to investigate the efficacy of an EEG-based MI BCI system coupled with MIT-Manus shoulder-elbow robotic feedback (BCI-Manus) for subjects with chronic stroke with upper-limb hemiparesis. In this single-blind, randomized trial, 26 hemiplegic subjects (Fugl-Meyer Assessment of Motor Recovery After Stroke [FMMA] score, 4-40; 16 men; mean age, 51.4 years; mean stroke duration, 297.4 days), prescreened with the ability to use the MI BCI, were randomly allocated to BCI-Manus or Manus therapy, lasting 18 hours over 4 weeks. Efficacy was measured using upper-extremity FMMA scores at weeks 0, 2, 4 and 12. ElEG data from subjects allocated to BCI-Manus were quantified using the revised brain symmetry index (rBSI) and analyzed for correlation with the improvements in FMMA score. Eleven and 15 subjects underwent BCI-Manus and Manus therapy, respectively. One subject in the Manus group dropped out. Mean total FMMA scores at weeks 0, 2, 4, and 12 weeks improved for both groups: 26.3 ± 10.3, 27.4 ± 12.0, 30.8 ± 13.8, and 31.5 ± 13.5 for BCI-Manus and 26.6 ± 18.9, 29.9 ± 20.6, 32.9 ± 21.4, and 33.9 ± 20.2 for Manus, with no intergroup differences (P = .51). More subjects attained further gains in FMMA scores at week 12 from BCI-Manus (7 of 11 [63.6%]) than Manus (5 of 14 [35.7%]). A negative correlation was found between the rBSI and FMMA score improvement (P = .044). BCI-Manus therapy was well tolerated and not associated with adverse events. In conclusion, BCI-Manus therapy is effective and safe for arm rehabilitation after severe poststroke hemiparesis. Motor gains were comparable to those attained with intensive robotic therapy (1,040 repetitions/session) despite reduced arm exercise repetitions using EEG-based MI-triggered robotic feedback (136 repetitions/session). The correlation of rBSI with motor improvements suggests that the rBSI can be used as a prognostic measure for BCI-based stroke rehabilitation.

Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke

The objective of this study was to investigate the efficacy of an Electroencephalography (EEG)-based Motor Imagery (MI) Brain-Computer Interface (BCI) coupled with a Haptic Knob (HK) robot for arm rehabilitation in stroke patients. In this three-arm, single-blind, randomized controlled trial; 21 chronic hemiplegic stroke patients (Fugl-Meyer Motor Assessment (FMMA) score 10–50), recruited after pre-screening for MI BCI ability, were randomly allocated to BCI-HK, HK or Standard Arm Therapy (SAT) groups. All groups received 18 sessions of intervention over 6 weeks, 3 sessions per week, 90 min per session. The BCI-HK group received 1 h of BCI coupled with HK intervention, and the HK group received 1 h of HK intervention per session. Both BCI-HK and HK groups received 120 trials of robot-assisted hand grasping and knob manipulation followed by 30 min of therapist-assisted arm mobilization. The SAT group received 1.5 h of therapist-assisted arm mobilization and forearm pronation-supination movements incorporating wrist control and grasp-release functions. In all, 14 males, 7 females, mean age 54.2 years, mean stroke duration 385.1 days, with baseline FMMA score 27.0 were recruited. The primary outcome measure was upper extremity FMMA scores measured mid-intervention at week 3, end-intervention at week 6, and follow-up at weeks 12 and 24. Seven, 8 and 7 subjects underwent BCI-HK, HK and SAT interventions respectively. FMMA score improved in all groups, but no intergroup differences were found at any time points. Significantly larger motor gains were observed in the BCI-HK group compared to the SAT group at weeks 3, 12, and 24, but motor gains in the HK group did not differ from the SAT group at any time point. In conclusion, BCI-HK is effective, safe, and may have the potential for enhancing motor recovery in chronic stroke when combined with therapist-assisted arm mobilization.

A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation

Non-invasive EEG-based motor imagery brain-computer interface (MI-BCI) holds promise to effectively restore motor control to stroke survivors. This clinical study investigates the effects of MI-BCI for upper limb robotic rehabilitation compared to standard robotic rehabilitation. The subjects are hemiparetic stroke patients with mean age of 50.2 and baseline Fugl-Meyer (FM) score 29.7 (out of 66, higher = 3D better) randomly assigned to each group respectively (N = 3D8 and 10). Each subject underwent 12 sessions of 1-hour rehabilitation for 4 weeks. Significant gains in FM scores were observed in both groups at post-rehabilitation (4.9, p = 3D0.001) and 2-month post-rehabilitation (4.9, p = 3D0.002). The experimental group yielded higher 2-month post-rehabilitation gain than the control (6.0 versus 4.0) but no significance was found (p = 3D0.475). However, among subjects with positive gain (N = 3D6 and 7), the initial difference of 2.8 between the two groups was increased to a significant 6.5 (p = 3D0.019) after adjustment for age and gender. Hence this study provides evidence that BCI-driven robotic rehabilitation is effective in restoring motor control for stroke.

A feasibility study of non-invasive motor-imagery BCI-based robotic rehabilitation for Stroke patients

This paper describes an initial study of non-invasive electroencephalograph (EEG)-based Brain Computer Interface (BCI) application on Stroke patients. The purpose of this study is to combine BCI and robotic arm for after-stroke rehabilitation exercises. A clinically-proven MANUS robotic rehabilitation shell is integrated with the NeuroComm BCI platform, whereby the robotic control mechanism is complemented by the motor imagery of the patient. 8 hemiparetic stroke patients with varying degrees of paralysis on the unilateral upper extremity are recruited for this study. The results show that most BCI-naïve hemiparetic stroke patients are capable of operating the BCI effectively, hence motivates further clinical studies on the extent of how BCI-based robotic rehabilitation are comparable with the control group that uses only robotic rehabilitation.

A clinical evaluation of non-invasive motor imagery-based brain-computer interface in stroke

This clinical study investigates whether the performance of hemiparetic stroke patients operating a non-invasive Motor Imagery-based Brain-Computer Interface (MI-BCI) is comparable to healthy subjects. The study is performed on 8 healthy subjects and 35 BCI-naïve hemiparetic stroke patients. This study also investigates whether the performance of the stroke patients in operating MI-BCI correlates with the extent of neurological disability. The performance is objectively computed from the 10×10-fold cross-validation accuracy of employing the Filter Bank Common Spatial Pattern (FBCSP) algorithm on their EEG measurements. The neurological disability is subjectively estimated using the Fugl-Meyer Assessment (FMA) of the upper extremity. The results show that the performance of BCI-naïve hemiparetic stroke patients is comparable to healthy subjects, and no correlation is found between the accuracy of their performance and their motor impairment in terms of FMA.

Detection of motor imagery of swallow EEG signals based on the dual-tree complex wavelet transform and adaptive model selection

OBJECTIVE Detection of motor imagery of hand/arm has been extensively studied for stroke rehabilitation. This paper firstly investigates the detection of motor imagery of swallow (MI-SW) and motor imagery of tongue protrusion (MI-Ton) in an attempt to find a novel solution for post-stroke dysphagia rehabilitation. Detection of MI-SW from a simple yet relevant modality such as MI-Ton is then investigated, motivated by the similarity in activation patterns between tongue movements and swallowing and there being fewer movement artifacts in performing tongue movements compared to swallowing. APPROACH Novel features were extracted based on the coefficients of the dual-tree complex wavelet transform to build multiple training models for detecting MI-SW. The session-to-session classification accuracy was boosted by adaptively selecting the training model to maximize the ratio of between-classes distances versus within-class distances, using features of training and evaluation data. MAIN RESULTS Our proposed method yielded averaged cross-validation (CV) classification accuracies of 70.89% and 73.79% for MI-SW and MI-Ton for ten healthy subjects, which are significantly better than the results from existing methods. In addition, averaged CV accuracies of 66.40% and 70.24% for MI-SW and MI-Ton were obtained for one stroke patient, demonstrating the detectability of MI-SW and MI-Ton from the idle state. Furthermore, averaged session-to-session classification accuracies of 72.08% and 70% were achieved for ten healthy subjects and one stroke patient using the MI-Ton model. SIGNIFICANCE These results and the subjectwise strong correlations in classification accuracies between MI-SW and MI-Ton demonstrated the feasibility of detecting MI-SW from MI-Ton models.

A clinical evaluation on the spatial patterns of non-invasive motor imagery-based brain-computer interface in stroke

This clinical study investigates whether the spatial patterns of hemiparetic stroke patients operating a non-invasive Motor Imagery-based Brain Computer Interface (MI-BCI) is comparable to healthy subjects. The spatial patterns for a specific frequency range are generated using the common spatial pattern (CSP) algorithm, of which is highly successful for discriminating two classes of EEG measurements in MI-BCI. The spatial patterns illustrate how the presumed sources project on the scalp and are effective in verifying the neurophysiological plausibility of the computed solution. The spatial patterns show focused activity in ipsilateral as well as contralateral hemisphere with respect to the hand by tapping or motor imagery in 2 BCI-artful healthy subjects and 12 BCI-naïve hemiparetic stroke patients. The results also show that neurophysiologically interpretable spatial patterns is more common in performing motor imagery compared to finger tapping by hemiparetic stroke patients. Hence, this shows that hemiparetic stroke patients are capable of operating MI-BCI.

A clinical study of motor imagery BCI performance in stroke by including calibration data from passive movement

Electroencephalogram (EEG) data from performing motor imagery are usually used to calibrate a subject-specific model in Motor Imagery Brain-Computer Interface (MI-BCI). However, the performance of MI is not directly observable by another person. Studies that attempted to address this issue in order to improve subjects with low MI performance had shown that it is feasible to use calibration data from Passive Movement (PM) to detect MI in healthy subjects. This study investigates the feasibility of using calibration data from PM of stroke patients to detect MI. EEG data from 2 calibration runs of MI and PM by a robotic haptic knob, and 1 evaluation run of MI were collected in one session of recording from 34 hemiparetic stroke patients recruited in the clinical study. In each run, 40 trials of MI or PM and 40 trials of the background rest were collected. The off-line run-to-run transfer kappa values from the calibration runs of MI, PM, and combined MI and PM, to the evaluation run of MI were then evaluated and compared. The results showed that calibration using PM (0.392) yielded significantly lower kappa value than the calibration using MI (0.457, p=4.40e-14). The results may be due to a significant disparity between the EEG data from PM and MI in stroke subjects. Nevertheless, the results showed that the calibration using both MI and PM (0.506) yielded significantly higher kappa value than the calibration using MI (0.457, p=9.54e-14). Hence, the results of this study suggest a promising direction to combine calibration data from PM and MI to improve MI detection on stroke.