Chuan Chu Wang

A Brain-Computer Interface Based Cognitive Training System for Healthy Elderly: A Randomized Control Pilot Study for Usability and Preliminary Efficacy

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 (nu200a=u200a15) and waitlist control arms (nu200a=u200a16). 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 (Mdnu200a=u200a4.0; p<0.001). Specifically, there were significant improvements in immediate memory (pu200a=u200a0.038), visuospatial/constructional (pu200a=u200a0.014), attention (pu200a=u200a0.039), 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. Trial Registration ClinicalTrials.gov NCT01661894

Dynamic initiation and dual-tree complex wavelet feature-based classification of motor imagery of swallow EEG signals

The use of motor imagery-based brain computer interface has recently been shown to have potential for rehabilitation. This paper proposes a novel scheme to detect motor imagery of swallow from electroencephalography (EEG) signals for dysphagia rehabilitation. The proposed scheme extracts features from the coefficients of dual-tree complex wavelet transform (DT-CWT). A novel sliding window-based peak localization scheme is proposed to dynamically locate the initiation of tongue movement from Electromyography (EMG) signal. Subsequently, effective time segments are extracted from EEG signal for classification based on the detected dynamic initiation location. Comparisons are made between our proposed scheme with that of the three existing approaches. The results based on six healthy subjects show that an increase in averaged accuracy of 9.95% is achieved. Further, an increase in averaged accuracy of 8.02% is resulted comparing our proposed scheme by using and not using the dynamic initiation to extract the time segments. Classification results using EMG data confirm that our results are not due to movements artifacts. Statistical tests with 95% confidence to estimate the accuracy on the respective action at chance level show that five out of six subjects performed above chance level for our proposed dynamic initiation and wavelet feature-based approach.

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

OBJECTIVEnDetection 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.nnnAPPROACHnNovel 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.nnnMAIN RESULTSnOur 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.nnnSIGNIFICANCEnThese 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.

Maximum dependency and minimum redundancy-based channel selection for motor imagery of walking EEG signal detection

This paper proposes a novel method to detect motor imagery of walking for the rehabilitation of stroke patients using the Laplacian derivatives (LAD) of power averaged across frequency bands as the feature. We propose to select the most correlated channels by jointly considering the mutual information between the LAD power features of the channels and the class labels, and the redundancy between the LAD power features of the channel with that of the selected channels. Experiments are conducted on the EEG data collected for 11 healthy subjects using proposed method and compared with existing methods. The results show that the proposed method yielded an average classification accuracy of 67.19% by selecting as few as 4 LAD channels. An improved result of 71.45% and 73.23% are achieved by selecting 10 and 22 LAD channels, respectively. Comparison results revealed significantly superior performance of our proposed method compared to that obtained using common spatial pattern and filter bank with power features. Most importantly, our proposed method achieves significant better accuracy for poor BCI performers compared to existing methods. Thus, the results demonstrated the potential of using the proposed method for detecting motor imagery of walking for the rehabilitation of stroke patients.

Detection of motor imagery of brisk walking from electroencephalogram.

Rehabilitation of lower limbs is equally as important as that of upper limbs. This paper presented a study to detect motor imagery of walking (MI-Walking) from background idle state. Broad overlapping neuronal networks involved in reorganization following motor imagery introduce redundancy. We hypothesized that MI-Walking could be robustly detected by constraining dependency among selected features and class separations. Hence, we proposed to jointly select channels and frequency bands involved in MI-Walking by optimizing/regularizing the objective function formulated on the dependency between features and class labels, redundancy between to-be-selected with selected features, and separations between classes, namely, regularized maximum dependency with minimum redundancy-based joint channel and frequency band selection (RMDR-JCFS). Evaluated on electroencephalography (EEG) data of 11 healthy subjects, the results showed that the selected channels were mainly located at premotor cortex, mid-central area overlaying supplementary motor area (SMA), prefrontal cortex, foot area sensory cortex and leg and arm sensorimotor representation area. Broad frequencies of alpha, mu and beta rhythms were involved. Our proposed method yielded an averaged accuracy of 76.67%, which was 9.08%, 5.03%, 7.03%, 14.15% and 3.88% higher than that obtained by common spatial pattern (CSP), filter-bank CSP, sliding window discriminate CSP, filter-bank power and maximum dependency and minimum redundancy methods, respectively. Further, our method yielded significantly superior performance compared with other channel selection methods, and it yielded an averaged session-to-session accuracy of 70.14%. These results demonstrated the potentials of detecting MI-Walking using proposed method for stroke rehabilitation.

On the Correlations of Motor Imagery of Swallow with Motor Imagery of Tongue Movements and Actual Swallow

This paper investigated the correlations between motor imagery of swallow (MI-SW) and motor imagery of tongue movements (MI-Ton), and correlations between MI-SW and actual swallow (Act-SW). EEG data of 10 healthy subjects and one dysphagia patient were collected and analyzed. The group analysis results of using bin-based spectral power demonstrated that MI-SW and MI-Ton, and MI-SW and Act-SW were strongly correlated (p-value < 0.001, examined at ‘C3’) for both mu and low beta frequency bands. Further, the correlation was weaken but still significant for MI-SW and Act-SW (p-value < 0.05), and MI-SW and MI-Ton (p-value < 0.01) for the dysphagia patient. These results validated the use of MI-SW and MI-Ton for dysphagia rehabilitation.