Prof GUAN Cuntai, PhD


Professor, Computer Science and Engineering


Program Director, Strategic Collaboration


Co-Director, NTU-NNI Neuro-technology Fellowship Program


Fellow, IEEE


School of Computer Science and Engineering


Nanyang Technological University, Singapore



Phone: (65) 6790 6205

Office: N4-02B-52





Research Areas:

o   Brain Computer Interfaces & Brain Machine Interfaces (algorithms, devices, systems, applications)

o   Neural Signal Processing

o   Neural Image Processing

o   Machine learning

o   Data Analytics

o   Artificial Intelligence

o   Neural and Cognitive Processes and Rehabilitation


Research topics:

o   Mental state detection and classification based on machine learning (deep learning, reinforcement learning, adaptation, etc.)

o   Applying BCI in clinical domains, such as neuro-rehabilitation, ADHD, anxiety, depression, sleep and wellness

o   Computational approaches to understand and quantify human cognition, emotion and preference

o   Brain-computer interfaces (non-invasive or invasive) as a replacement or enhancement to motor deficiency

o   Medical image-based analysis, detection, prediction and diagnosis

o   Artificial Intelligence for big data analysis

o   Knowledge transfer from human learning (human intelligence) to machine learning (artificial intelligence)


Current and past projects:

o   Effectiveness of a Brain-Computer Interface-based Programme for the Treatment of Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorders in Children: A Pilot Study.

o   Development of Interactive System for Brain-Computer Interfaces

o   Brain-computer Interface in Cognition and Rehabilitation

o   Arousal Detection and Training for Social Anxiety Disorder (SAD)

o   Robust Neural Decoding and Control System for a Brain Machine Interface

o   iWENS: Intelligent Wearable Neural Sensing System

o   SPIE: Scent Preference Identification based on EEG

o   Brain-computer Interface System for Training Memory and Attention in Healthy Elderly and Elderly with Mild Cognitive Impairment

o   Simultaneous Multiscale Hyperspectral Near-infrared (NIR) Optical Imaging and MRI for Functional and Molecular Imaging

o   EEG-Based Brain Computer Interface for Cognitive Enhancement in Elderly with Age-Related Cognitive Decline and Mild Cognitive impairment (3E-COG)

o   NeuroDevice Phase I: Neural Signal Processing

o   ArtsBCI: Advanced Rehabilitation Therapy for Stroke based on Brain Computer Interface

o   iSyncc: Intelligent System for Neural Critical Care

o   Brain Computer Interface for Attention Deficit Hyperactivity Disorder (ADHD) Treatment

o   Effectiveness of a Brain-Computer Interface-based Treatment for Attention Deficit and Hyperactivity Disorder (ADHD)

o   BCI-Based Robotic Rehabilitation for Stroke

o   Brainy Communicator

o   Noninvasive Brain Stimulation and Brain-Computer Interface Assisted Motor Imagery for Rehabilitation of Mobility After Stroke

o   A Brain-Computer Interface Based Intervention versus Sham Intervention for the Treatment of ADHD – a Double-Blind Randomized Controlled Trial

o   Assistive Soft Robotic Glove Intervention using Brain-Computer Interface for Elderly Stroke Patients

o   Affect Regulation Based on Brain-computer Interface Towards Treatment for Depression

o   Feasibility Study of BCI-based Adaptive Sensing and Feedback Training for Chronic Pain Management

o   Functional Magnetic Resonance Imaging Investigation of the Effects of Brain-Computer Interfaced Based Training on Selective Attention and Response Inhibition in Children with Attention Deficit Hyperactivity Disorder (ADHD)

o   Combined Transcranial Direct Current Stimulation and Motor Imagery-based Robotic Arm Training for Stroke Rehabilitation – a Feasibility Study

o   Automated Video-EEG Analytic System in Seizure Detection in the Epilepsy Monitoring Unit


Highlights of Research:


Deep Learning

o   [Deep Learning] Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks (Siavash Sakhavi, Cuntai Guan, and Shuicheng Yan), IEEE Transactions on Neural Networks and Learning SystemsVol 29, No.  11, Nov. 2018, pp 5619-5629. ViewPDF


Abstract— Deep learning (DL) methods and architectures have been the state-of-the-art classification algorithms for computer vision and natural language processing problems. However, the successful application of these methods in motor imagery (MI) brain–computer interfaces (BCIs), in order to boost classification performance, is still limited. In this paper, we propose a classification framework for MI data by introducing a new temporal representation of the data and also utilizing a convolutional neural network (CNN) architecture for classification. The new representation is generated from modifying the filter-bank common spatial patterns method, and the CNN is designed and optimized accordingly for the representation. Our framework outperforms the best classification method in the literature on the BCI competition IV-2a 4-class MI data set by 7% increase in average subject accuracy. Furthermore, by studying the convolutional weights of the trained networks, we gain an insight into the temporal characteristics of EEG.


o   [Deep Learning] Inter-subject transfer learning with end-to-end deep convolutional neural network for EEG-based BCI (Fatemeh Fahimi, Zhuo Zhang, Wooi Boon Goh, Tih-Shih Lee, Kai Keng Ang and Cuntai Guan), Journal of Neural Engineering, 2019, 16(9), 026007. ViewPDF


Abstract— Objective: Despite the effective application of deep learning in brain-computer interface (BCI) systems, the successful execution of this technique especially for inter-subject classification in cognitive BCI has not been accomplished yet. In this paper, we propose a framework based on deep convolutional neural network (CNN) to detect attentive mental state from single-channel raw electroencephalography (EEG) data. Approach: We develop an end-to-end deep CNN to decode the attentional information from EEG time-series. We also explore the consequence of input representations on the performance of deep CNN by feeding three different EEG representations into the network. To ensure the practical application of the proposed framework and avoid time-consuming re-trainings, we perform inter-subject transfer learning techniques as classification strategy. Eventually, to interpret the learned attentional patterns, we visualize and analyze the network perception of attention and nonattention classes. Main results: The average classification accuracy is 79.26% with only 15.83% of 120 subjects having the accuracy below 70% (a generally accepted threshold for BCI). This is while with inter-subject approach, it is literally hard to output high classification accuracy. This end-to-end classification framework surpasses the conventional classification methods for attention detection. The visualization results validate that the learned patterns from raw data are meaningful.



Schematic diagram of end-to-end CNN-based classification framework for transfer learning.



Comparing the performance of baseline and end-to-end deep CNN methods in attention detection. Classification framework based on deep CNN (both strategies; LOO and subject adaptation) significantly outperforms the baseline methods.


o   [Deep Learning] Convolutional Neural Network-based Transfer Learning and Knowledge Distillation using Multi-Subject Data in Motor Imagery BCI (Siavash Sakhavi and Cuntai Guan), 8th International IEEE EMBS Conference on Neural Engineering (NER), Shanghai, China, May 25-28, 2017. ViewPDF


Abstract— In Brain Computer Interfaces (BCIs), with multiple recordings from different subjects in hand, a question arises regarding whether the knowledge of previously recorded subjects can be transferred to a new subject. In this study, we explore the possibility of transferring knowledge by using a convolutional network model trained on multiple subjects and fine-tuning the model on a small amount of data from a new subject, thus, reducing the calibration time by reducing the time needed to record data and train a model. Our results show a significant increase in 4-class classification accuracy on the BCI IV-2a competition data, even when a small subset of the data is provided for training.



o   [Deep Learning] Parallel Convolutional-Linear Neural Network For Motor Imagery Classification In Brain-Computer Interfaces (Siavash Sakhavi, Cuntai Guan, Shuicheng Yan), European Signal Processing Conference (EUSIPCO), Nice, France, 31 Aug – 4 Sept, 2015. ViewPDF


Abstract - Deep learning, recently, has been successfully applied to image classification, object recognition and speech recognition. However, the benefits of deep learning and accompanying architectures have been largely unknown for BCI applications. In motor imagery-based BCI, an energy-based feature, typically after spatial filtering, is commonly used for classification. Although this feature corresponds to the estimate of event-related synchronization/desynchronization in the brain, it neglects energy dynamics which may contain valuable discriminative information. Because traditional classification methods, such as SVM, cannot handle this dynamical property, we proposed an architecture that inputs a dynamic energy representation of EEG data and utilizes convolutional neural networks for classification. By combining this network with a static energy network, we saw a significant increase in performance. We evaluated the proposed method and compared with SVM on a multi-class motor imagery dataset (BCI competition dataset IV-2a). Our method outperforms SVM with static energy features significantly (p < 0.01).


o   [Deep Learning] On the Use of Convolutional Neural Networks and Augmented CSP Features for Multi-class Motor Imagery of EEG Signals Classification (Huijuan Yang, Siavash Sakhavi, Kai Keng Ang, Cuntai Guan), 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’15), Milan, Italy, 25-29 Aug, 2015. ViewPDF


Abstract— Learning the deep structures and unknown correlations is important for the detection of motor imagery of EEG signals (MI-EEG). This study investigates the use of convolutional neural networks (CNNs) for the classification of multi-class MI-EEG signals. Augmented common spatial pattern (ACSP) features are generated based on pair-wise projection matrices, which covers various frequency ranges. We propose a frequency complementary feature map selection (FCMS) scheme by constraining the dependency among frequency bands. Experiments are conducted on BCI competition IV dataset IIa with 9 subjects. Averaged cross-validation accuracy of 68.45% and 69.27% is achieved for FCMS and all feature maps, respectively, which is significantly higher (4.53% and 5.34%) than random map selection and higher (1.44% and 2.26%) than filter-bank CSP (FBCSP). The results demonstrate that the CNNs are capable of learning discriminant, deep structure features for EEG classification without relying on the handcrafted features.



BCI for Stroke Rehabilitation


o   [BCI for Stroke Rehabilitation]  Prognostic and Monitory EEG-Biomarkers for BCI Upper-limb Stroke Rehabilitation (Ravikiran Mane, Effie Chew, Kok Soon Phua, Kai Keng Ang, Neethu Robinson, Vinod A. P., Cuntai Guan), IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, ViewPDF


Abstract— With the availability of multiple rehabilitative interventions, identifying the one that elicits the best motor outcome based on the unique neuro-clinical profile of the stroke survivor is a challenging task. Predicting the potential of recovery using biomarkers specific to an intervention hence becomes important. To address this, we investigate intervention-specific prognostic and monitory biomarkers of motor function improvements using quantitative electroencephalography (QEEG) features in 19 chronic stroke patients following two different upper extremity rehabilitative interventions viz. Brain-Computer Interface (BCI) and transcranial Direct Current Stimulation coupled BCI (tDCSBCI). Brain symmetry index was found to be the best prognostic QEEG for clinical gains following BCI intervention (r = -0.80, p= 0.02), whereas power ratio index (PRI) was observed to be the best predictor for tDCS-BCI (r = -0.96, p = 0.004) intervention. Importantly, statistically significant between-intervention differences observed in the predictive capabilities of these features suggest that intervention-specific biomarkers can be identified. This approach can be further pursued to distinctly predict the expected response of a patient to available interventions. The intervention with the highest predicted gains may then be recommended to the patient, thereby enabling a personalized rehabilitation regime.


Statistically significant relationships for the prediction of intervention gains. Significant correlation between pre-intervention EEG features and two week FMA gains for the tDCS-BCI group are presented in panels (a)-(c), whereas panels (d)-(e) represent the same relationship for the BCI group. Similarly, significant association between four-week FMA gains and pre-intervention EEG features is displayed in panels (f)-(h) for the tDCS-BCI group and panels (i)-(j) for the BCI group.



Statistically significant relationships for monitoring the evolution of intervention gains. All the panels represent the significant association observed between two-week intervention gains and intervention-induced changes in the EEG features in BCI group. No significant relationships where observed in the tDCS-BCI group.


o   [BCI for Stroke Rehabilitation]  Assessment of the Efficacy of EEG-based MI-BCI with Visual Feedback and EEG Correlates of Mental Fatigue for Upper-Limb Stroke Rehabilitation (Ruyi Foong, Kai Keng Ang, Chai Quek, Cuntai Guan, Kok Soon Phua, Christopher Wee Keong Kuah, Vishwanath Arun Deshmukh, Lester Hon Lum Yam, Deshan Kumar Rajeswaran, Ning Tang, Effie Chew, Karen Sui Geok Chua), IEEE Transactions on Biomedical Engineering, 2019, ViewPDF


Abstract— Objective: This single-arm multisite trial investigates the efficacy of the Neurostyle Brain Exercise Therapy Towards Enhanced Recovery (nBETTER) system, an Electroencephalogram (EEG)-based Motor Imagery Brain-Computer Interface (MI-BCI) employing visual feedback, for upper-limb stroke rehabilitation, and the presence of EEG correlates of mental fatigue during BCI usage. Methods: Thirteen recruited stroke patients underwent thrice-weekly nBETTER therapy coupled with standard arm therapy over 6 weeks. Upper extremity Fugl-Meyer Motor Assessment (FMA) scores were measured at baseline (Week 0), post-intervention (Week 6) and follow-ups (Weeks 12 and 24). In total, 11/13 patients (mean age 55.2 years old, mean post-stroke duration 333.7 days, mean baseline FMA 35.5) completed the study. Results: Significant FMA gains relative to baseline were observed at Weeks 6 and 24. Retrospectively comparing to the standard arm therapy (SAT) control group and BCI with haptic knob (BCI-HK) intervention group from a previous similar study, the SAT group had no significant gains whereas the BCI-HK group had significant gains at Weeks 6, 12 and 24. EEG analysis revealed significant positive correlations between relative beta power and BCI performance in the frontal and central brain regions, suggesting that mental fatigue may contribute to poorer BCI performance. Conclusion: nBETTER, an EEG-based MI-BCI employing only visual feedback, helps stroke survivors sustain short-term FMA improvement. Analysis of EEG relative beta power indicates that mental fatigue may be present. Significance: This study adds nBETTER to the growing literature of safe and effective stroke rehabilitation MI-BCI, and suggests an additional fatigue monitoring role in future such BCI.







o   [BCI for Stroke Rehabilitation]  Motor imagery-assisted brain-computer interface for gait retraining in neurorehabilitation in chronic stroke (Ning Tang, Cuntai Guan, Kai Keng Ang, Kok Soon Phua, Effie Chew), Annals of Physical and Rehabilitation Medicine, 61, July, 2018. ViewPDF


Abstract— Subjects (n = 13) with more than 9 months post-stroke and Functional Ambulation Category 3–4 underwent 12 sessions of MI-BCI gait training, at a frequency of thrice a week. Subjects were instructed to perform a MI task whereby they imagined themselves walking properly with both legs. If the MI task is performed correctly as detected via electroencephalography acquisition, a pair of cartoon footprints in the monitor will be activated to walk forward. Each MI-BCI session includes 160 MI trials with resting interval every 40 trials. Timed up-to-go test and 10 meter walk test, as well as the resting motor threshold measured by TMS were performed before, after and 6 weeks after MI-BCI gait training. Results showed that MI-BCI was safe and well tolerated by stroke subjects. Both walking speed and balance improved after MI-BCI gait training (Fig. 1). This was in line with an increase in the corticospinal activity in the contralesional M1 motor cortex (Fig. 2).


                        (Fig. 2)                                              (Fig. 1)



o   [BCI for Stroke Rehabilitation] EEG-based Strategies to Detect Motor Imagery for Control and Rehabilitation (Kai Keng Ang and Cuntai Guan), IEEE Transactions on Neural Systems and Rehabilitation EngineeringVol 25, No. 4, April 2017, pp 392-401 (Invited Paper) ViewPDF


Abstract—Advances in brain–computer interface (BCI) technology have facilitated the detection of Motor Imagery (MI) from electroencephalography (EEG). First, we present three strategies of using BCI to detect MI from EEG: operant conditioning that employed a fixed model, machine learning that employed a subject-specific model computed from calibration, and adaptive strategy that continuously compute the subject-specific model. Second, we review prevailing works that employed the operant conditioning and machine learning strategies. Third, we present our past work on six stroke patients who underwent a BCI rehabilitation clinical trial with averaged accuracies of 79.8% during calibration and 69.5% across 18 online feedback sessions. Finally, we perform an offline study in this paper on our work employing the adaptive strategy. The results yielded significant improvements of 12% (p < 0.001) and 9% (p < 0.001) using all the data and using limited preceding data respectively in the feedback accuracies. The results showed an increase in the amount of training data yielded improvements. Nevertheless, results of using limited preceding data showed a larger part of the improvement was due to the adaptive strategy and changing subject-specific models did not deteriorate the accuracies. Hence the adaptive strategy is effective in addressing the non-stationarity between calibration and feedback sessions.


Adaptive strategy of continuously computing the subject-specific model from the EEG data recorded during calibration as well as the feedback sessions to perform MI detection for control and rehabilitation. The lines illustrated in gray show the differences from the machine learning strategy.


o   [BCI for Stroke Rehabilitation] Brain-computer Interface for Neuro-Rehabilitation of Upper Limb after Stroke (Kai Keng Ang and Cuntai Guan), Proceedings of the IEEE, vol. 103, no. 6, pp. 944-953, Jun. 2015. (Invited Paper) ViewPDF


Abstract - Current rehabilitation therapies for stroke rely on physical practice (PP) by the patients. Motor imagery (MI), the imagination of movements without physical action, presents an alternate neurorehabilitation for stroke patients without relying on residue movements. However, MI is an endogenous mental process that is not physically observable. Recently, advances in brain–computer interface (BCI) technology have enabled the objective detection of MI that spearheaded this alternate neurorehabilitation for stroke. In this review, we present two strategies of using BCI for neurorehabilitation after stroke: detecting MI to trigger a feedback, and detecting MI with a robot to provide concomitant MI and PP. We also present three randomized control trials that employed these two strategies for upper limb rehabilitation. A total of 125 chronic stroke patients were screened over six years. The BCI screening revealed that 103 (82%) patients can use electroencephalogram-based BCI, and 75 (60%) performed well with accuracies above 70%. A total of 67 patients were recruited to complete one of the three RCTs ranging from two to six weeks of which 26 patients, who underwent BCI neurorehabilitation that employed these two strategies, had significant motor improvement of 4.5 measured by Fugl-Meyer Motor Assessment of the upper extremity. Hence, the results demonstrate clinical efficacy of using BCI as an alternate neurorehabilitation for stroke.


Strategy of using the brain–computer interface (BCI) to detect motor imagery (MI) to trigger a feedback for neurorehabilitation in stroke.



Strategy of using the BCI to detect MI with a robot to provide concomitant MI and PP for neurorehabilitation in stroke.


o   [BCI for Stroke Rehabilitation] Facilitating Effects of Transcranial Direct Current Stimulation on Motor Imagery Brain-Computer Interface for Stroke Rehabilitation (Kai Keng Ang, Cuntai Guan, Chuanchu Wang, Ling Zhao, Wei Peng Teo, Chang Wu Chen, Yee Sien Ng, Effie Chew), Archives of Physical Medicine and Rehabilitation,Vol 96 (3 Suppl 1), S79-87, 2015. (Invited Paper) ViewPDF


Abstract - Objective: To investigate the efficacy and effects of transcranial direct current stimulation (tDCS) on motor imagery brain-computer interface (MI-BCI) with robotic feedback for stroke rehabilitation. Design: A sham-controlled, randomized controlled trial. Setting: Patients recruited through a hospital stroke rehabilitation program. Participants: Subjects (NZ19) who incurred a stroke 0.8 to 4.3 years prior, with moderate to severe upper extremity functional impairment, and passed BCI screening. Interventions: Ten sessions of 20 minutes of tDCS or sham before 1 hour of MI-BCI with robotic feedback upper limb stroke rehabilitation for 2 weeks. Each rehabilitation session comprised 8 minutes of evaluation and 1 hour of therapy. Main Outcome Measures: Upper extremity Fugl-Meyer Motor Assessment (FMMA) scores measured end-intervention at week 2 and follow-up at week 4, online BCI accuracies from the evaluation part, and laterality coefficients of the electroencephalogram (EEG) from the therapy part of the 10 rehabilitation sessions. Results: FMMA score improved in both groups at week 4, but no intergroup differences were found at any time points. Online accuracies of the evaluation part from the tDCS group were significantly higher than those from the sham group. The EEG laterality coefficients from the therapy part of the tDCS group were significantly higher than those of the sham group. Conclusions: The results suggest a role for tDCS in facilitating motor imagery in stroke.


o   [BCI for Stroke Rehabilitation] Xin Hong, Zhongkang Lu, Irvin Teh, Fatima Ali Nasrallah, Wei Peng Teo, Kai Keng Ang, Kok Soon Phua, Cuntai Guan, Effie Chew, and Kai-Hsiang Chuang, “Brain plasticity following MI-BCI training combined with tDCS in a randomized trial in chronic subcortical stroke subjects: a preliminary study”, Scientific Reports (Nature Publishing Group), 7, 2017 doi:10.1038/s41598-017-08928-5 ViewPDF


Abstract - Brain-computer interface-assisted motor imagery (MI-BCI) or transcranial direct current stimulation (tDCS) has been used in stroke rehabilitation, though their combinatory effect is unknown. We investigated brain plasticity following a combined MI-BCI and tDCS intervention in chronic subcortical stroke patients with unilateral upper limb disability. Nineteen patients were randomized into tDCS and sham-tDCS groups. Diffusion and perfusion MRI, and transcranial magnetic stimulation were used to study structural connectivity, cerebral blood flow (CBF), and corticospinal excitability, respectively, before and 4 weeks after the 2-week intervention. After quality control, thirteen subjects were included in the CBF analysis. Eleven healthy controls underwent 2 sessions of MRI for reproducibility study. Whereas motor performance showed comparable improvement, long-lasting neuroplasticity can only be detected in the tDCS group, where white matter integrity in the ipsilesional corticospinal tract and bilateral corpus callosum was increased but sensorimotor CBF was decreased, particularly in the ipsilesional side. CBF change in the bilateral parietal cortices also correlated with motor function improvement, consistent with the increased white matter integrity in the corpus callosum connecting these regions, suggesting an involvement of interhemispheric interaction. The preliminary results indicate that tDCS may facilitate neuroplasticity and suggest the potential for refining rehabilitation strategies for stroke patients.


o   [BCI for 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 (Kai Keng Ang, Cuntai Guan, Kok Soon Phua, Chuanchu Wang, Longjiang Zhou, Ka Yin Tang, Gopal Joseph Ephraim_Joseph, Christopher Wee Keong Kuah and Karen Sui Geok Chua), Frontiers in NeuroEngineering, 2014, 7:30. doi: 10.3389/fneng.2014.00030. ViewPDF


Abstract - 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.



o   [BCI for Stroke Rehabilitation] Resting State Changes In Functional Connectivity Correlate With Movement Recovery For BCI And Robot-Assisted Upper Extremity Training After Stroke (Balint Varkuti, Cuntai Guan, Yaozhang Pan, Kok Soon Phua, Kai Keng Ang, Christopher Kuah, Karen Chua, Beng Ti Ang, Niels Birbaumer, Ranganatha Sitaram), Neurorehabilitation and Neural Repairvol. 27, no. 1, 2013, pp 53-62 ViewPDF


Abstract - Background. Robot-assisted training may improve motor function in some hemiparetic patients after stroke, but no physiological predictor of rehabilitation progress is reliable. Resting state functional magnetic resonance imaging (RS-fMRI) may serve as a method to assess and predict changes in the motor network. Objective. The authors examined the effects of upper-extremity robot-assisted rehabilitation (MANUS) versus an electroencephalography-based brain computer interface setup with motor imagery (MI EEG-BCI) and compared pretreatment and posttreatment RS-fMRI. Methods. In all, 9 adults with upper-extremity paresis were trained for 4 weeks with a MANUS shoulder-elbow robotic rehabilitation paradigm. In 3 participants, robot-assisted movement began if no voluntary movement was initiated within 2 s. In 6 participants, MI-BCI– based movement was initiated if motor imagery was detected. RS-fMRI and Fugl-Meyer (FM) upper-extremity motor score were assessed before and after training. Results. The individual gain in FM scores over 12 weeks could be predicted from functional connectivity changes (FCCs) based on the pre-post differences in RS-fMRI measurements. Both the FM gain and FCC were numerically higher in the MI-BCI group. Increases in FC of the supplementary motor area, the contralesional and ipsilesional motor cortex, and parts of the visuospatial system with mostly association cortex regions and the cerebellum correlated with individual upper-extremity function improvement. Conclusion. FCC may predict the steepness of individual motor gains. Future training could therefore focus on directly inducing these beneficial increases in FC. Evaluation of the treatment groups suggests that MI is a potential facilitator of such neuroplasticity.

Illustration of the method: generation of FC change (FCC) maps from subtraction of 2 IC maps, each representing the magnitude of FC to a component center by image intensity. Abbreviations: IC, independent component; FC, functional connectivity.



FC between green and red areas has changed (indicated by white arrows); FCC in green regions predicted individual motor gain. Abbreviations: SMA, supplementary motor area; FCC, functional connectivity change.


o   [BCI for Stroke Rehabilitation]  Kai Keng Ang, Cuntai Guan, Karen Sui Geok Chua, Beng Ti Ang, Christopher Wee Keong Kuah, Chuanchu Wang, Koksoon Phua, Zheng Yang Chin, Haihong Zhang, “A Large Clinical Study on the Ability of Stroke Patients in Operating EEG-based Motor Imagery Brain-Computer Interface”, Clinical EEG and Neuroscience, October, 2011, pp 253-258 ViewPDF


Abstract - Brain-computer interface (BCI) technology has the prospects of helping stroke survivors by enabling the interaction with their environ - ment 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.




BCI for ADHD and Cognition

o   [BCI for ADHD] A Randomized Controlled Trial of a Brain-Computer Interface based Attention Training Program for ADHD (Choon Guan Lim, Xue Wei Wendy Poh, Daniel Shuen Sheng Fung, Cuntai Guan, Dianne Bautista, Yin Bun Cheung, Haihong Zhang, Si Ning Yeo, Ranga Krishnan, Tih Shih Lee), PLOS ONE14 (5): e0216225, 2019. ViewPDF


Abstract – Objective:  The use of brain-computer interface in neurofeedback therapy for attention deficit hyperactivity disorder (ADHD) is a relatively new approach. We conducted a randomized controlled trial (RCT) to determine whether an 8-week brain computer interface (BCI)-based attention training program improved inattentive symptoms in children with ADHD compared to a waitlist-control group, and the effects of a subsequent 12-week lower-intensity training. Study design: We randomized 172 children aged 6–12 attending an outpatient child psychiatry clinic diagnosed with inattentive or combined subtypes of ADHD and not receiving concurrent pharmacotherapy or behavioral intervention to either the intervention or waitlist-control group. Intervention involved 3 sessions of BCI-based training for 8 weeks, followed by 3 training sessions per month over the subsequent 12 weeks. The waitlist-control group received similar 20-week intervention after a wait-time of 8 weeks. Results: The participants’ mean age was 8.6 years (SD = 1.51), with 147 males (85.5%) and 25 females (14.5%). Modified intention to treat analyzes conducted on 163 participants with at least one follow-up rating showed that at 8 weeks, clinician-rated inattentive symptoms on the ADHD-Rating Scale (ADHD-RS) was reduced by 3.5 (SD 3.97) in the intervention group compared to 1.9 (SD 4.42) in the waitlist-control group (between-group difference of 1.6; 95% CI 0.3 to 2.9 p = 0.0177). At the end of the full 20-week treatment, the mean reduction (pre-post BCI) of the pooled group was 3.2 (95% CI 2.4 to 4.1). Conclusion: The results suggest that the BCI-based attention training program can improve ADHD symptoms after a minimum of 24 sessions and maintenance training may sustain this improvement. This intervention may be an option for treating milder cases or as an adjunctive treatment.


Δ = change score; Δ > 0 indicates improvement, Δ = 0 no change and Δ < 0 deterioration; in Waitlist control group, Δ is calculated from Week 8 when the child starts intervention, for changes at end of intervention (week 28) and end of trial (week 32).

a Mean difference, independent two-sample t-test

b Mean of change scores from pooled BCI-treatment and Waitlist control groups

c One-sample t-test result to test null hypothesis that mean score is zero




o   [BCI for Cognition] Effectiveness of a personalised Brain-Computer Interface system for cognitive training in healthy elderly: a randomized controlled trial (Si Ning Yeo, Tih Shih Lee, Wei Theng Sng, Min Quan Heo, Dianne Bautista, Yin Bun Cheung, Haihong Zhang, Zheng Yang Chin, Lei Feng, Helen Juan Zhou, Mei Sian Chong, Tze Pin Ng, Ranga Krishnan, Cuntai Guan), Journal of Alzheimer's Disease, 66(1), 2018, pp127-138. ViewPDF


Abstract - Background: Cognitive training has been demonstrated to improve cognitive performance in older adults. To date, no study has explored personalized training that targets the brain activity of each individual. Objective: This is the first large-scale trial that examines the usefulness of personalized neurofeedback cognitive training. Methods: We conducted a randomized-controlled trial with participants who were 60–80 years old, with Clinical Dementia Rating (CDR) score of 0–0.5, Mini-Mental State Examination (MMSE) score of 24 and above, and with no neuropsychiatric diagnosis. Participants were randomly assigned to the Intervention or Waitlist-Control group. The training system, BRAINMEM, has attention, working memory, and delayed recall game components. The intervention schedule comprised 24 sessions over eight weeks and three monthly booster sessions. The primary outcome was the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) total score after the 24-session training. Results: There were no significant between-subject differences in overall cognitive performance post-intervention. However, a sex moderation effect (p = 0.014) was present. Men in the intervention group performed better than those in the waitlist group (mean difference, +4.03 (95% CI 0.1 to 8.0), p = 0.046. Among females, however, bothwaitlist-control and intervention participants improved from baseline, although the between-group difference in improvement did not reach significance. BRAINMEM also received positive appraisal and intervention adherence from the participants.

Screenshots of the BRAINMEM interface (L-R from top: Home page, Shopping List, Card Matching, Shopping List Recall [item selection], Shopping List Recall [free-recall], Face Matching).



o   [BCI for ADHD] Brain-computer-interface-based intervention re-normalizes brain functional network topology in children with attention deficit/hyperactivity disorder (Xing Qian, Beatrice Rui Yi Loo, Francisco Xavier Castellanos, Siwei Liu, Hui Li Koh, Xue Wei Wendy Poh, Ranga Krishnan, Daniel Fung, Michael Chee, Cuntai Guan, Tih-Shih Lee, Choon Guan Lim, Juan Zhou), Translational Psychiatry (Nature Publishing Group)8(1), 2018 ViewOnline



Abstract - A brain-computer-interface (BCI)-based attention training game system has shown promise for treating attention deficit/hyperactivity disorder (ADHD) children with inattentive symptoms. However, little is known about brain network organizational changes underlying behavior improvement following BCI-based training. To cover this gap, we aimed to examine the topological alterations of large-scale brain functional networks induced by the 8-week BCI-based attention intervention in ADHD boys using resting-state functional magnetic resonance imaging method. Compared to the non-intervention (ADHD-NI) group, the intervention group (ADHD-I) showed greater reduction of inattention symptoms accompanied with differential brain network reorganizations after training. Specifically, the ADHD-NI group had increased functional connectivity (FC) within the salience/ventral attention network (SVN) and increased FC between task-positive networks (including the SVN, dorsal attention (DAN), somatomotor, and executive control network) and subcortical regions; in contrast ADHD-I group did not have this pattern. In parallel, ADHD-I group had reduced degree centrality and clustering coefficient as well as increased closeness in task-positive and the default mode networks (prefrontal regions) after the training. More importantly, these reduced local functional processing mainly in the SVN were associated with less inattentive/internalizing problems after 8-week BCI-based intervention across ADHD patients. Our findings suggest that the BCI-based attention training facilitates behavioral improvement in ADHD children by reorganizing brain functional network from more regular to more random configurations, particularly renormalizing salience network processing. Future long-term longitudinal neuroimaging studies are needed to develop the BCI-based intervention approach to promote brain maturation in ADHD.

Study design schematic diagram. A) Participants were randomly divided into two groups: intervention group (ADHD-I) and non-intervention group (ADHD-NI). All participants underwent resting-state functional magnetic resonance imaging (RS-fMRI) and neuropsychological assessments at baseline and follow-ups. Between the two visits, participants in ADHD-I group underwent a brain-computer-interface (BCI)-based attention game training (three sessions per week for 8 weeks). B) The functional connectivity (FC) matrix among 141 regions of interest (ROIs) covering the whole brain was derived for each participant at each time point. Intra- and inter-network FC measures were calculated. The FC matrix was then thresholded to a sparse weighted network to derive network topological measures. These FC metrics were then used to examine the effect of the BCI-based intervention on brain networks and brain-behavioral associations


o   [BCI for Cognition] A Pilot Randomized Controlled Trial Using EEG-based Brain–computer Interface Training for a Chinese-speaking Group of Healthy Elderly (Tih Shih Lee, Shin Yi Quek, Siau Juinn Alexa Goh, Rachel Phillips, Cuntai Guan, Yin Bun Cheung, Lei Feng, Chuanchu Wang, Zheng Yang Chin, Haihong Zhang, Jimmy Lee, Tze Pin Ng, Ranga Krishnan)Clinical Interventions in Aging, 2015:10, pp 217–227 ViewPDF  


Abstract - Background: There is growing evidence that cognitive training (CT) can improve the cognitive functioning of the elderly. CT may be influenced by cultural and linguistic factors, but research examining CT programs has mostly been conducted on Western populations. We have developed an innovative electroencephalography (EEG)-based brain–computer interface (BCI) CT program that has shown preliminary efficacy in improving cognition in 32 healthy English-speaking elderly adults in Singapore. In this second pilot trial, we examine the accept­ability, safety, and preliminary efficacy of our BCI CT program in healthy Chinese-speaking Singaporean elderly. Methods: Thirty-nine elderly participants were randomized into intervention (n=21) and wait­list control (n=18) arms. Intervention consisted of 24 half-hour sessions with our BCI-based CT training system to be completed in 8 weeks; the control arm received the same intervention after an initial 8-week waiting period. At the end of the training, a usability and acceptability questionnaire was administered. Efficacy was measured using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), which was translated and culturally adapted for the Chinese-speaking local population. Users were asked about any adverse events experienced after each session as a safety measure. Results: The training was deemed easily usable and acceptable by senior users. The median difference in the change scores pre- and post-training of the modified RBANS total score was 8.0 (95% confidence interval [CI]: 0.0–16.0, P=0.042) higher in the intervention arm than waitlist control, while the mean difference was 9.0 (95% CI: 1.7–16.2, P=0.017). Ten (30.3%) participants reported a total of 16 adverse events – all of which were graded “mild” except for one graded “moderate”. Conclusion: Our BCI training system shows potential in improving cognition in both English- and Chinese-speaking elderly, and deserves further evaluation in a Phase III trial. Overall, participants responded positively on the usability and acceptability questionnaire.



Changes in RBANS domain and total scale index scores between pre- and post-intervention, pooling data from the intervention and waitlist control groups


Change in RBANS scores between Weeks 1 and 8

Summary statistics (n36)


RBANS domain index scores



Immediate memory



Mean (SD)

4.1 (16.0)


Median (range)

1.5 (-23 to 39)





Mean (SD)

1.7 (11.3)


Median (range)

0.0 (-19 to 44)





Mean (SD)

0.22 (16.6)


Median (range)

0.0 (-45 to 36)





Mean (SD)

4.1 (9.3)


Median (range)

1.5 (-12 to 34)


Delayed memory



Mean (SD)

3.1 (9.1)


Median (range)

0.0 (-19 to 24)


RBANS total scale index score



Mean (SD)

4.1 (10.4)


Median (range)

1.0 (-15 to 29)


Note: aP-value from Wilcoxon signed-rank test.

Abbreviations: RBANS, Repeatable Battery for the Assessment of Neuropsychological Status; SD, standard deviation.



o   [BCI for Cognition] A Brain-computer Interface Based Cognitive Training System for Healthy Elderly: A Randomized Control Pilot Study for Usability and Preliminary Efficacy (Tih-Shih Lee, Siau Juinn Alexa Goh, Shin Yi Quek, Rachel Phillips, Cuntai Guan, Yin Bun Cheung, Lei Feng, Stephanie Sze Wei Teng, Chuan Chu Wang, Zheng Yang Chin, Haihong Zhang, Tze Pin Ng, Jimmy Lee, Richard Keefe, Ranga Rama Krishnan), PLOS ONE, 2013, Vol 8, No 11, page e79419 ViewPDF


Abstract - Cognitive decline in aging is a pressing issue associated with significant healthcare costs and deterioration in quality of life. Previously, we reported the successful use of a novel brain-computer interface (BCI) training system in improving symptoms of attention deficit hyperactivity disorder. Here, we examine the feasibility of the BCI system with a new game that incorporates memory training in improving memory and attention in a pilot sample of healthy elderly. This study investigates the safety, usability and acceptability of our BCI system to elderly, and obtains an efficacy estimate to warrant a phase III trial. Thirty-one healthy elderly were randomized into intervention (n = 15) and waitlist control arms (n = 16). Intervention consisted of an 8-week training comprising 24 half-hour sessions. A usability and acceptability questionnaire was administered at the end of training. Safety was investigated by querying users about adverse events after every session. Efficacy of the system was measured by the change of total score from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) before and after training. Feedback on the usability and acceptability questionnaire was positive. No adverse events were reported for all participants across all sessions. Though the median difference in the RBANS change scores between arms was not statistically significant, an effect size of 0.6SD was obtained, which reflects potential clinical utility according to Simon’s randomized phase II trial design. Pooled data from both arms also showed that the median change in total scores pre and post-training was statistically significant (Mdn = 4.0; p<0.001). Specifically, there were significant improvements in immediate memory (p = 0.038), visuospatial/constructional (p = 0.014), attention (p = 0.039), and delayed memory (p<0.001) scores. Our BCI-based system shows promise in improving memory and attention in healthy elderly, and appears to be safe, user-friendly and acceptable to senior users. Given the efficacy signal, a phase III trial is warranted.

A model engaged in the Brain Computer Interface (BCI) memory and attention training game system


Plot of observed RBANS median total score over time by treatment arm


o   [BCI for ADHD]  A Brain-Computer Interface Based Attention Training Program for Treating Attention Deficit Hyperactivity Disorder (Choon Guan Lim, Tih Shih Lee, Cuntai Guan, Daniel Fung, Yudong Zhao, Stephanie Sze Wei Teng, Haihong Zhang,  Ranga Krishnan), PLOS ONE, 2012, 7(10): e46692 ViewPDF


Abstract - Attention deficit hyperactivity disorder (ADHD) symptoms can be difficult to treat. We previously reported that a 20-session brain-computer interface (BCI) attention training programme improved ADHD symptoms. Here, we investigated a new more intensive BCI-based attention training game system on 20 unmedicated ADHD children (16 males, 4 females) with significant inattentive symptoms (combined and inattentive ADHD subtypes). This new system monitored attention through a head band with dry EEG sensors, which was used to drive a feed forward game. The system was calibrated for each user by measuring the EEG parameters during a Stroop task. Treatment consisted of an 8-week training comprising 24 sessions followed by 3 once-monthly booster training sessions. Following intervention, both parent-rated inattentive and hyperactive-impulsive symptoms on the ADHD Rating Scale showed significant improvement. At week 8, the mean improvement was 24.6 (5.9) and 24.7 (5.6) respectively for inattentive symptoms and hyperactive-impulsive symptoms (both p <0.01). Cohen’s d effect size for inattentive symptoms was large at 0.78 at week 8 and 0.84 at week 24 (post boosters). Further analysis showed that the change in the EEG based BCI ADHD severity measure correlated with the change ADHD Rating Scale scores. The BCI-based attention training game system is a potential new treatment for ADHD.

A model engaged in intervention with the Brain-Computer Interface (BCI) attention training game system


Mean ADHD Rating Scale IV (ARS-IV) Scores as rated by parents