Towards a Gamified Therapeutic Brain-Computer Interface for Children with Gait Impairment

Central nervous system (CNS) disorders cause over 1 billion people to live with a life-altering handicap. Some CNS disorders, such as cerebral palsy and spina bifida, affect one-four per 1000 and one per 2758 children respectively, according to the Centers for Disease Control. These pediatric CNS disorders leave patients with many years of living with partial or complete motor impairment. Brain-computer interfaces (BCIs) have been researched as tools for rehabilitation for adults with disabilities due to neurological disease, brain injury or amputation; however, research on the design of BCI systems for children has not received the same level of attention by the scientific community. This is unfortunate as the developing brain is very plastic, thus, children may be the best candidates for BCIs for neurorehabilitation. The primary aim of this project was to adapt a system, developed in the Laboratory for Non-Invasive Brain-Machine Interface Systems at the University of Houston, that can be used for BCI system development for children. Such a system will provide real-time data capture from two types of sensors (scalp electroencephalography or EEG, and joint angle data from the lower limbs) during treadmill walking while providing real-time visual feedback of the child’s gait pattern via a digital avatar. To achieve this aim, a system was created in the MATLAB programming environment that initializes, acquires and synchronizes EEG and joint angles, and then, filters and sends joint angles to control the digital avatar and in parallel, stores time-locked unprocessed EEG and joint angle data for offline processing - the first step in designing a BCI system. Applications of the system include, but are not limited to, investigating the neural representations for motor control in children, and extracting neural and kinematic features for diagnostic purposes and for the design of closed-loop BCI systems for children.