Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors.

This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG)-based brain machine interface (BMI). Intent was inferred from movement related cortical potentials (MRCPs) measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II), to guide movement and to encourage active user participation by providing instantaneous sensory feedback.

Current Trends in Robot-Assisted Upper-Limb Stroke Rehabilitation: Promoting Patient Engagement in Therapy.

Stroke is one of the leading causes of long-term disability today; therefore, many research efforts are focused on designing maximally effective and efficient treatment methods. In particular, robotic stroke rehabilitation has received significant attention for upper-limb therapy due to its ability to provide high-intensity repetitive movement therapy with less effort than would be required for traditional methods. Recent research has focused on increasing patient engagement in therapy, which has been shown to be important for inducing neural plasticity to facilitate recovery.

Tactile Feedback of Object Slip Facilitates Virtual Object Manipulation.

Recent advances in myoelectric prosthetic technology have enabled more complex movements and interactions with objects, but the lack of natural haptic feedback makes object manipulation difficult to perform. Our research effort aims to develop haptic feedback systems for improving user performance in object manipulation. Specifically, in this work, we explore the effectiveness of vibratory tactile feedback of slip information for grasping objects without slipping.