Non-medical Brain-Computer Interfaces (BCIs) are gaining popularity as neurophysiological devices, capable of measuring users state, become more affordable and effective. BCI technology has been integrated into applications for various purposes such as device control, user-state monitoring, training and education, cognitive improvement, neurophysiological evaluation, and gaming. Although there are BCI software platforms (BCISP) that assist experienced developers with creating applications, there are few options designed for novice BCI feedback application developers wanting to create custom applications. The current lack of novice-friendly BCI development tools may present a barrier to entry for users interested in getting started with developing neurofeedback applications.

This research explores the concept of using block-based programming (BBP) to aid novice programmers with creating BCI feedback applications. This work presents a BBP environment with electroencephalogram (EEG) sensing features that was designed and implemented based on insights gained from previous BCI and visual programming languages literature. This system was evaluated in a formal study that tasked forty-two novice programmers with developing neurofeedback applications. Three exercises were designed to investigate participants' efficiency and effectiveness with the developed BBP environment. BCI self-efficacy, end-user programming barriers, and usability were also evaluated during the study. The results of this study suggest that although novice programmers encounter barriers related to creating block-based affective state mathematical functions, they find creating neurofeedback applications with the visual environment easy. These results also suggest that BCI self-efficacy may serve as a predictor of the amount of time it takes novice developers to create neurofeedback applications prior to being exposed to the BBP environment.