Yogesh Kumar Meena

Brain-machine interface (BMI) driven robot-assisted neurorehabilitation intervention has demonstrated improvement in upper-limb (UL) motor function, specifically, with post-stroke hemiparetic patients. However, neurophysiological patterns related to such interventions are not well understood. This study examined the longitudinal changes in band-limited resting-state (RS) functional connectivity (FC) networks in association with post-stroke UL functional recovery achieved by a multimodal intervention involving motor attempt (MA) based BMI and robotic hand-exoskeleton. Four adults were rehabilitated with the intervention for a period lasting upto 6 weeks. RS magnetoencephalography (MEG) signals, Action Research Arm Test (ARAT), and grip strength (GS) measures were recorded at five equispaced sessions over the intervention period. An average post-interventional increase of 100.0% (p=0.00028) and 88.0% were attained for ARAT and GS, respectively. A cluster-based statistical test involving correlation estimates between beta-band (15–26 Hz) RS-MEG FCs and UL functional recovery provided positively correlated sub-networks in both contralesional and ipsilesional motor cortices. The fronto-parietal FC exhibited hemispheric lateralisation wherein majority of the positively and negatively correlated connections were found in contralesional and ipsilesional hemispheres, respectively. Our findings are consistent with the theory of bilateral motor cortical association with UL recovery and predict novel FC patterns that can be important for higher level cognitive functions.

This paper presents an underactuated design of a robotic hand exoskeleton and a challenge based neurorehabilitation strategy. The exoskeleton is designed to reproduce natural human fingertip paths during extension and grasping, keeping minimal kinematic complexity. It facilitates an impedance adaptation based trigged assistance control strategy by a switching between active non-assist and passive assistance modes. In active non-assist mode, the exoskeleton motion follows the applied fingertip forces based on an impedance model. If the applied fingertip forces are inadequate, the passive assistance mode is triggered. The impedance parameters are updated at regular intervals based on the user performance, to implement a challenge based rehabilitation strategy. A six-week long hand therapy, conducted on four chronic stroke patients results in significant (p-value<0.05) increase in force generation capacity and decrease (p-value<0.05) in the required assistance. Also, there was a significant (p-value<0.05) increase in the system impedance parameters which adequately challenged the patients. The change in the Action-Research-Arm-Test (ARAT) scores from baseline are also found to be significant (p-value<0.05) and beyond the minimal clinically important difference (MCID) limit. Thus the results prove that the proposed control strategy with has the potential to be a clinically effective solution for personalized rehabilitation of poststroke hand functionality.

Yogesh Kumar Meena, Anirban Chowdhury, Ujjwal Sharma, Hubert Cecotti, Braj Bhushan, Ashish Dutta & Girijesh Prasad

Virtual keyboard applications and alternative communication devices provide new means of communication to assist disabled people. To date, virtual keyboard optimization schemes based on script-specific information along with multimodal input access facility are limited. In this work, we propose a novel method for optimizing the position of the displayed items for gaze-controlled tree-based menu selection systems by considering a combination of letter frequency and command selection time. The optimized graphical user interface (GUI) layout has been designed for a Hindi language virtual keyboard based on a menu wherein 10 commands provide access to type 88 different characters along with additional text editing commands. The system can be controlled in two different modes: eye-tracking alone and eye-tracking with an access soft-switch. Five different keyboard layouts have been presented and evaluated with ten healthy participants. Further, the two best performing keyboard layouts have been evaluated with eye-tracking alone on ten stroke patients. The overall performance analysis demonstrated significantly superior typing performance, high usability (87% SUS score), and low workload (NASA TLX with 17 scores) for the letter frequency and time-based organization with script specific arrangement design. This work represents the first optimized gaze-controlled Hindi virtual keyboard, which can be extended to other languages.

A major issue in electroencephalogram (EEG) based brain-computer interfaces (BCIs) is the intrinsic non-stationarities in the brain waves, which may degrade the performance of the classifier, while transitioning from calibration to feedback generation phase. The non-stationary nature of the EEG data may cause its input probability distribution to vary over time, which often appear as a covariate shift. To adapt to the covariate shift, we had proposed an adaptive learning method in our previous work and tested it on offline standard datasets. This paper presents an online BCI system using previously developed covariate shift detection (CSD)-based adaptive classifier to discriminate between mental tasks and generate neurofeedback in the form of visual and exoskeleton motion. The CSD test helps prevent unnecessary retraining of the classifier. The feasibility of the developed online-BCI system was first tested on 10 healthy individuals, and then on 10 stroke patients having hand disability. A comparison of the proposed online CSD-based adaptive classifier with conventional non-adaptive classifier has shown a significantly (p≤0.01) higher classification accuracy in both the cases of healthy and patient groups. The results demonstrate that the online CSD-based adaptive BCI system is superior to the non-adaptive BCI system and it is feasible to be used for actuating hand exoskeleton for the stroke-rehabilitation applications.