ABSTRACT
Neurological rehabilitation is a physician-supervised programme for individuals with nervous system diseases, injuries or disorders. Neurological rehabilitation, also known as neurorehabilitation, is part of the rehabilitation process that improves function, reduces severity and enhances a patient's well-being. Because neurological injuries occur in the brain, spine and nerves, affecting multiple body parts including organs, blood vessels, muscles and bones, rehabilitation requires a multidisciplinary approach. This study conducted a systematic literature review (SLR) on the use of mobile game in neurorehabilitation. The steps undertaken in the literature review included the collection, identification, categorisation, summarisation and synthesis of relevant studies in the research domain. A total of 50 related articles were reviewed. The study identified that the effects on cognitive skills, handgrip strength, memory, attention, visuospatial abilities, executive function tasks, motor functionality, and improvements in balance, visual perception, and functional mobility are impacts of the use of mobile games in neurological rehabilitation. Furthermore, several research challenges and recommendations for future research were identified.
ABSTRACT
The brain's functional connectivity (FC) estimated at sensor level from electromagnetic (EEG/MEG) signals can provide quick and useful information towards understanding cognition and brain disorders. Volume conduction (VC) is a fundamental issue in FC analysis due to the effects of instantaneous correlations. FC methods based on the imaginary part of the coherence (iCOH) of any two signals are readily robust to VC effects, but neglecting the real part of the coherence leads to negligible FC when the processes are truly connected but with zero or π-phase (modulus 2π) interaction. We ameliorate this issue by proposing a novel method that implements an envelope of the imaginary coherence (EIC) to approximate the coherence estimate of supposedly active underlying sources. We compare EIC with state-of-the-art FC measures that included lagged coherence, iCOH, phase lag index (PLI) and weighted PLI (wPLI), using bivariate autoregressive and stochastic neural mass models. Additionally, we create realistic simulations where three and five regions were mapped on a template cortical surface and synthetic MEG signals were obtained after computing the electromagnetic leadfield. With this simulation and comparison study, we also demonstrate the feasibility of sensor FC analysis using receiver operating curve analysis whilst varying the signal's noise level. However, these results should be interpreted with caution given the known limitations of the sensor-based FC approach. Overall, we found that EIC and iCOH demonstrate superior results with most accurate FC maps. As they complement each other in different scenarios, that will be important to study normal and diseased brain activity.
