Channel Selection Improves Accuracy for Pediatric Users of Motor Imagery Brain-Computer Interfaces

Children are an under-served population in the field of brain-computer interface (BCI) development. The high prevalence of lifelong disability coupled with the diversity and plasticity of children's brains make them ideal candidates for personalized BCI systems. Channel selection methods provide a tool for the in-session personalization of BCI systems. To evaluate the efficacy of channel selection for pediatric users, we tested four wrapper-based channel selection algorithms, sequential forward selection (SFS), sequential backward selection (SBS), sequential forward floating selection (SFFS), and sequential backward floating selection (SBFS) on offline motor imagery BCI data from three datasets involving typically developing children. The purpose was to assess the performance benefits and computational costs of each algorithm. All algorithms provided classification accuracy gains of 10–15 % with their optimal subsets. The time required to reach the optimal subsets varied between algorithms, but all took less than 80 s with mean completion times of 9.5 s and 35.8 s for the fastest (SFS) and slowest (SFFS), respectively. Adjusting the stopping criterion of the algorithm enables users to further reduce computation time with a disproportionately small effect on classification accuracy. All methods demonstrated an ability to prioritize expected physiological regions of interest and leave out channels detrimental to the classifier. Channel selection offers personalization of the BCI system for a specific user and a specific classifier. These findings emphasize the value of using personalized channel selection algorithms to improve motor imagery BCI systems for pediatric users.