Stroke is a severe health issue, and motor recovery after stroke remains an important challenge in the rehabilitation field. Neurofeedback (NFB), as part of a brain–computer interface, is a technique for modulating brain activity using on-line feedback that has proved to be useful in motor rehabilitation for the chronic stroke population in addition to traditional therapies. Nevertheless, its use and applications in the field still leave unresolved questions. The brain pathophysiological mechanisms after stroke remain partly unknown, and the possibilities for intervention on these mechanisms to promote cerebral plasticity are limited in clinical practice. In NFB motor rehabilitation, the aim is to adapt the therapy to the patient’s clinical context using brain imaging, considering the time after stroke, the localization of brain lesions, and their clinical impact, while taking into account currently used biomarkers and technical limitations. These modern techniques also allow a better understanding of the physiopathology and neuroplasticity of the brain after stroke. We conducted a narrative literature review of studies using NFB for post-stroke motor rehabilitation. The main goal was to decompose all the elements that can be modified in NFB therapies, which can lead to their adaptation according to the patient’s context and according to the current technological limits. Adaptation and individualization of care could derive from this analysis to better meet the patients’ needs. We focused on and highlighted the various clinical and technological components considering the most recent experiments. The second goal was to propose general recommendations and enhance the limits and perspectives to improve our general knowledge in the field and allow clinical applications. We highlighted the multidisciplinary approach of this work by combining engineering abilities and medical experience. Engineering development is essential for the available technological tools and aims to increase neuroscience knowledge in the NFB topic. This technological development was born out of the real clinical need to provide complementary therapeutic solutions to a public health problem, considering the actual clinical context of the post-stroke patient and the practical limits resulting from it.

In this study we present a modified version of t commercially-available myoelectric prosthesis (Myobock © , Ottobock) with the aim of providing a Brain-Machine Interface BMI-based sensorimotor control of this device. The new system uses as input the ElectroEncephaloGraphy (EEG) signals of the user as well as vibrations produced by a bracelet containing vibrating motors whose frequencies are proportional to the forces measured by Force-Sensitive Resistors (FSR) installed on the fingertips of the prosthesis. Four combinations of three different feature extraction methods (CSP, WD, GSO) have been used to construct the feature vectors of the EEG signals collected by two different recording systems with different number of electrodes during the experiments performed with seven able-bodied and four amputee subjects. The classification/prediction performances of three machine learning algorithms (Artificial Neural Network, Support Vector Machine with linear and Radial Basis Function kernels) were then tested. The reported results provide a proof of concept for the use of a wireless BMI to control the main types of movement of myoelectric prostheses using an EEG system with less electrodes rather than a research-grade system.