Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

We investigated the feasibility of a programmable soft pneumatic robot in modulating brain network dynamics in people who have a stroke. Diverse modes of human-robot interaction and fNIRS-based graph theory analysis are used. To begin, turn on the robotic system and connect the two air pump power cables to the power outlet and the soft pneumatic glove.

Assist the participant in wearing the soft pneumatic robot on the affected hand, ensuring a secure fit around the palm and fingers. Fasten the robot using the Velcro strap, positioning it securely from the dorsal side of the thumb to thenar eminence on the palmar side. Now select the appropriate inflation/deflation mode on the control interface.

Confirm the parameter settings without causing discomfort to the participant. Use the continuous wave functional near-infrared spectroscopy system to record data from all participants. Emit near infrared light at wavelengths of 690 nanometers and 830 nanometers to penetrate two to three centimeters beneath the cerebral cortex with a sampling frequency of 100 hertz.

Now select an appropriately sized functional near-infrared spectroscopy cap. Position the sensor number label slightly above the center of the forehead and align the FpZ and Cz optodes of the 10-20 electroencephalography or EEG system. Secure the detectors and light sources using a flexible headband to ensure optimal contact with the skin.

Instruct the participant to remain in a quiet, relaxed state with their head still and eyes open during data collection. Before starting formal data collection, instruct the participant to rest quietly for two minutes without falling asleep. Select the Experimental Protocol from the software interface on the computer connected to the functional near-infrared spectroscopy system.

Perform signal calibration to minimize light leakage, targeting a signal strength of 75%or higher with minimal noise. If calibration fails, adjust the placement of the sensor and perform the calibration again. The optode sensors contain light-emitting diodes and photo detectors designed to emit and detect near infrared light at specific wavelengths.

From the software interface, start cerebral cortex data recording while instructing the participant to stay relaxed and avoid moving their head. Begin the experimental task following the randomized sequence and continuously collect data on cerebral oxygenation and hemodynamics. Once the task is completed, stop the data acquisition and securely save the recorded experimental data.

Then create three-dimensional brain activation maps by visualizing the changes in oxyhemoglobin concentration across brain regions. Use anatomical landmarks and the International 10-20 system to project the functional near-infrared spectroscopy channels onto the corresponding brain areas. Finally, extract graph theory metrics, including clustering coefficient, average path length, Small World Index, global efficiency, degree centrality, and eigenvector centrality from the functional connectivity matrices derived from functional near-infrared spectroscopy data.

A total of 10 individuals with stroke were enrolled in the study and underwent resting state assessment, slow-mode robotic therapy, and fast-mode robotic therapy in a randomized order. Following interaction with the soft pneumatic robot, significant improvements were observed in clustering coefficient, average path length, and global efficiency, while Small World Index, degree centrality, and eigenvector centrality did not change significantly. Soft robotic therapy effectively modulates the topological organization of distributed brain network in clustering coefficient, average path length, and global efficiency.

These results underscore the potential of theory soft robotic intervention to enhance neuroconnectivity and facilitate brain network reorganization in stroke rehabilitation. We find robotic-assisted rehabilitation interventions enhancing functional outcomes for the stroke survivors. These should integrate advanced neuroimaging and computational techniques to tailor interventions to patients'neurophysiological profiles.