Behavioral Markers of Movement Quality in Immersive Environments
Decades of work are behind us in the fields of therapy and rehabilitation development for conditions brought on by neurodegenerative diseases. However, most of the developments have been unsatisfactory because of the lack of impact on life quality, post-disability. An important step in such research was the implementation of brain-computer interface (BCI) devices for therapeutic purposes. In such settings, individuals would engage in therapy that was commonly based on methods to enhance motor imagery that has underlying neural mechanisms involved with actual movement and therefore can be used in its restoration. Going a step further, a physical rehabilitation practice that is becoming prevalent in medical neuroinformatics research is the combined use of BCI and virtual reality (VR) in creating dynamic environments that allow for accelerated progress in motor rehabilitation, or recovery. The BCI-VR system allows users to control external devices such as their ‘virtual hands’, while also adding the immersion factor, which allows for a more natural-like, intuitive, and real-time feedback. Because of its immersion factor, the implementation of VR in BCI is making the motor rehabilitation process more engaging for the patient and has been shown to improve the efficiency of the system .
In this research, we will be exploring the construct of movement quality (MQ) through an EEG-based BCI-VR. Healthy participants will be performing a series of tasks in a virtual environment, targeted at their upper limbs, simulating physical therapy tasks. In this setting, MQ can be operationalized through neuronal markers such as oscillation strengths, onset times, and localizations, as well as by assessing behavioral markers. This research focuses on assessing behavioral markers, namely task time and accuracy, as separate entities as well as their correlation with each other and with neuronal markers. The specific research questions being posed are, firstly, the correlation of behavioral and neuronal markers. We hypothesize that neuronal markers of MQ will be present along with behavioral markers of MQ, or rather that they will positively correlate. Secondly, we will examine training-induced differences in movement quality, or movement quality optimization (MQO). We hypothesize MQO could be observed in lower task time and higher accuracy, with participants having more tasks completed.
Finally, the significance of many previous and current research lies in the potential implications of the results and their usability in a real-life, medicinal setting.
 W. Dong, Y. Fan, S.H. Hsu, J. Xu, Y. Zhou, J. Tao, X. Lan, and F. Li, “Combining Brain–Computer Interface and Virtual Reality for Rehabilitation in Neurological Diseases: A Narrative Review.” Annals of Physical and Rehabilitation Medicine, vol. 64, pp. 101404, 2021. doi:10.1016/j.rehab.2020.03.015