Evaluation of the Usability of a Serious Game in Virtual Reality with a Focus on the Perception and Experience of Health Professionals for Motor Rehabilitation in Children with Cerebral Palsy.

Introduction: Cerebral palsy (CP) is a nonprogressive neuropathological condition that requires lifelong neurocognitive-motor rehabilitation. Evidences indicate that the use of new technologies to assist in rehabilitation processes, such as serious games in virtual reality (VR), have served as adjuncts to therapy and capable of promoting engagement, motivation, and motor activation for these patients. Objective: To investigate the usability of a serious game in VR to help with the stability and balance of the head and trunk of children with CP, focusing on the perception and experience of health professionals. Methods: The collection was carried out with health professionals, and the results were comprehensively evaluated through viability by means of the total score, number of correct answers, number of errors, and level of difficulty during the execution of the game, which were collected from the performance report generated by the application. System satisfaction was also verified by the System Usability Scale (SUS). Results: The mean obtained from the total score of the SUS was 82.10 ± 12.66 points, being considered of high usability for the suggested purpose. The professionals' opinion about the usability of the system did not change due to the performance during the game. Conclusion: The study demonstrated that the developed rehabilitation program has successfully delivered the experience to exercise the head control and trunk balance of subjects with CP.

EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs.

Brain-computer interface (BCI) technology has emerged as an influential communication tool with extensive applications across numerous fields, including entertainment, marketing, mental state monitoring, and particularly medical neurorehabilitation. Despite its immense potential, the reliability of BCI systems is challenged by the intricacies of data collection, environmental factors, and noisy interferences, making the interpretation of high-dimensional electroencephalogram (EEG) data a pressing issue. While the current trends in research have leant towards improving classification using deep learning-based models, our study proposes the use of new features based on EEG amplitude modulation (AM) dynamics. Experiments on an active BCI dataset comprised seven mental tasks to show the importance of the proposed features, as well as their complementarity to conventional power spectral features. Through combining the seven mental tasks, 21 binary classification tests were explored. In 17 of these 21 tests, the addition of the proposed features significantly improved classifier performance relative to using power spectral density (PSD) features only. Specifically, the average kappa score for these classifications increased from 0.57 to 0.62 using the combined feature set. An examination of the top-selected features showed the predominance of the AM-based measures, comprising over 77% of the top-ranked features. We conclude this paper with an in-depth analysis of these top-ranked features and discuss their potential for use in neurophysiology.