Longitudinal Validation of Neurofeedback in Stroke Motor Rehabilitation Through Brain Imaging
Longitudinal Validation of Neurofeedback in Stroke Motor Rehabilitation Through Brain Imaging
With the main goal of generalising findings into Virtual Reality-Neurofeedback-Motor imagery (VR-NF-MI) system, this project aims to develop a new motor rehabilitation tool, for the upper limb, allied to the use of rising of information and communication technologies (ICT). By identifying correlations on the neural activity, during motor imagery and through brain imaging (fMRI), with distinct training protocols and feedback, these protocols are developed to create user-specific models that later can be used in NF-MI rehabilitation sessions.
Every year millions of people worldwide suffer from stroke, being one of the leading causes of death and longterm disability. This leads to cognitive and motor impairments, resulting in loss of independence in their daily life together with an additional psychological impact in mood disorders and depression. Evolving to a chronic condition, stroke requires continuous rehabilitation and therapy. Personalised Virtual-Reality (VR) approaches have been shown to accelerate the recovery process compared to non-Information and communication technologies (ICT) based interventions. However, most of these novel VR approaches are suitable only for a reduced subset of patients, generally those with better recovery prognostics and better motor control. Thus, the idea of training the central nervous system was established, through EEG-based neurofeedback (NF) and motor-imagery (MI). Although the benefits of MI-NF have been illustrated in a plethora of studies, the reduced ability for stroke patients to use NF does not allow an accurate control, reducing the capabilities of MI-NF systems. The aim of this project is to develop a novel and more inclusive rehabilitation system with the use of novel ICT technologies, in order to overcome current limitations. This will be achieved by identifying the neural correlates of motor action during motor imagery through brain imaging (fMRI), and differences in brain activation with different training feedback protocols for formulating user-specific models that will be used later in NF-MI rehabilitation sessions. This will facilitate the use of neural interfaces to train the central nervous system; specifically, the investigators will develop a personalized EEG-based immersive NF through VR for MI training. The ultimate goal is to generalize the findings into a VR-NF-MI training paradigm for both admitted and ambulatory patients as well as continued domestic care.
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