Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient

Abstract

Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the user's movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.

Brain-machine interfaces for rehabilitation in stroke: A review

“…Stroke patients present relatively well preserved afferent pathways that should be used to exploit functional neuroplastic mechanisms. A better understanding of how the motor networks are stimulated and combined feedback modalities (e.g., a robotic exoskeleton and electrical stimulation) might allow a more precise stimulation of the neural network involved in movement (Del-Ama et al, 2012;Hortal et al, 2015;Resquín et al, 2016). Furthermore, stimulation at the brain level might also address the pathological stroke-related interhemispheric imbalances, boost the effects of BMI therapies, and facilitate plasticity and recovery (Johnson et al, 2017).…”

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

“…Medical : This is the most popular research area in this SLR, with 193 articles. g.Tec equipment was used in 54 articles for the EEG method [ 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 ], 33 articles used Emotiv equipment [ ...…”

Electroencephalography-guided upper-limb hybrid robotic platform to modulate cortical excitability

“…The hybrid robotic system used in the experiments for this study was previously presented in [13], [15]. In brief, this system consists of three sub-blocks: the hybrid assistive block (a passive Exoskeleton and FES), the high-level control (HLC) block, and a visual feedback interface.…”

“…The arm's reference trajectory was generated using a mathematical expression called minimum jerk [16], which assumes that the arm is moved smoothly from one point to another using a bell-shaped velocity profile. The feedback error learning (FEL) control algorithm was implemented to modulate the pulse width of the electrical signal applied over the arm's muscles (see [13], [15] for further details).…”