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Huang Y.Y.,Nanyang Technological University | Low K.H.,Nanyang Technological University | McGregor A.H.,Imperial College London | Kong K.H.,Tan Tock Seng Hospital Rehabilitation Center
Advanced Robotics | Year: 2011

A successful implementation of any effective rehabilitation process using robotics systems must consider both the clinical and engineering issues. Useful results obtained by clinical groups are not widely received in the robotics community. Many engineering works documented do not seem to focus on the crucial clinical problems. The lack of an effective means of communication between the clinical and engineering groups is a major hindrance to any effective rehabilitation process. The main objective of this study is to quantify hand strength in patients experiencing motor disability and loss of function in the hand (e.g., following stroke and spinal cord injury). In the present work, hand strength is measured by using surface electromyography and a force sensor during gripping/pinch tasks. With the development of a robotics system, which will obtain inputs from the clinician's assessment, together with the pre-measurement results at each progress interval and through an interactive control system, the robot will provide appropriate assistance to the patient to achieve specific tasks. This study presents clinical data from hand strength measurements that have been processed and streamlined to facilitate interpretation and characterization, by virtue of the design of experiments and multivariate data analysis data analysis methods. The proposed analysis is able to provide useful data (both statistical data and graphic information) for objective and quantitative assessment towards control applications on the hand rehabilitation device that is being developed. © Koninklijke Brill NV, Leiden, 2011. Source

Tan H.G.,Institute for Infocomm Research | Kong K.H.,Tan Tock Seng Hospital Rehabilitation Center | Shee C.Y.,Nanyang Technological University | Wang C.C.,Institute for Infocomm Research | And 2 more authors.
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 | Year: 2010

Through certain mental actions, our electroencephalogram (EEG) can be regulated to operate a brain-computer interface (BCI), which translates the EEG patterns into commands that can be used to operate devices such as prostheses. This allows paralyzed persons to gain direct brain control of the paretic limb, which could open up many possibilities for rehabilitative and assistive applications. When using a BCI neuroprosthesis in stroke, one question that has surfaced is whether stroke patients are able to produce a sufficient change in EEG that can be used as a control signal to operate a prosthesis. The aim of this paper is to determine if post-acute (<3 months) stroke patients are able to use an electroencephalogram (EEG)-based BCI to trigger neuromuscular electrical stimulation (NMES)-assisted extension of the wrist and fingers. EEG was recorded while subjects performed motor imagery of their paretic limb, and then analyzed to determine the optimal frequency range within the mu-rhythm that showed the greatest attenuation. With the help of visual feedback, subjects then trained to regulate their mu-rhythm EEG to operate the BCI to trigger NMES on their wrist extensor muscles. 9 post-acute (<3 months) stroke patients, aged 58.2 ± 9.3 yrs, participated in this study. 4 out of 6 subjects who completed the trial are able to use the BCI to trigger NMES on their paretic wrist extensor muscles. This study presents findings that movement intention, as characterized by the attenuation of mu-rhythm EEG, is detectable in post-acute stroke patients, and that this signal is can be used as a control signal for the patients to operate a BCI to trigger NMES. © 2010 IEEE. Source

Yang H.,Institute for Infocomm Research | Guan C.,Institute for Infocomm Research | Chua K.S.G.,Tan Tock Seng Hospital Rehabilitation Center | Chok S.S.,Tan Tock Seng Hospital Rehabilitation Center | And 4 more authors.
Journal of Neural Engineering | Year: 2014

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. © 2014 IOP Publishing Ltd. Source

Lambercy O.,ETH Zurich | Dovat L.,National University of Singapore | Yun H.,Tan Tock Seng Hospital Rehabilitation Center | Wee S.K.,Tan Tock Seng Hospital Rehabilitation Center | And 6 more authors.
i-CREATe 2010 - International Convention on Rehabilitation Engineering and Assistive Technology | Year: 2010

This paper investigates the assessment of hand function after stroke using the HapticKnob, an end-effector based robotic device to train grasping and forearm pronation/supination. A method to extract meaningful parameters to evaluate hand function from kinematic data recorded by the robot during rehabilitation exercises is presented. Step-wise regression analysis has been performed in an attempt to reconstruct clinical assessment scores from the kinematic data collected during a 6-week rehabilitation therapy with the HapticKnob. Good correlations between clinical and reconstructed scores (r=0.669 for Fugl-Meyer Assessment, r=0.689 for Motricity Index, r=0.599 for Motor Assessment Scale, and r=0.792 for Modified Ashworth Scale) illustrate the potential of these objective measures to quantitatively evaluate hand motor function. This offers new possibilities to monitor patients' progress and customize exercise challenge during rehabilitation therapy. © 2010 START Centre. Source

Ang K.K.,Institute for Infocomm Research | Chua K.S.G.,Tan Tock Seng Hospital Rehabilitation Center | Phua K.S.,Institute for Infocomm Research | Wang C.,Institute for Infocomm Research | And 4 more authors.
Clinical EEG and Neuroscience | Year: 2015

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. © EEG and Clinical Neuroscience Society. Source

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