Neural interfaces are clearly an emerging topic of study for researchers of human-machine interaction. NIs have reached a level of usability that now necessitates a clear, systematic and empirical approach to the design of their interfaces. As Desney Tan and Anton Nijholt write, “(Neural Interfaces) are now mature enough that HMI researchers must add them to their tool belt when designing novel input techniques.” Yet in spite of the maturity of the technology, little has been done in creating easy-to-use neural interfaces.

One reason why NI have yet to be fully integrated into the HMI research paradigm is because of the lack of possibilities to undertake large-scale empirical iterations of interface design. As NI continue to largely remain in neuroengineering labs, where back-end algorithmic development is favored over front-end development, and tests are limited to a few engineering students and a handful of users in the clinic, there is simply not enough focus or opportunity to build robust testing conditions for the development of efficient and user-friendly interfaces. Improving the user experience of NI will have direct effects on the user’s ability to gain control over a machine using only his or her mind. Our research into new training and feedback technologies using HMI and user experience design methodologies, together with exploring the role of embodiment for improving control, could be the tipping point to bring NI to a larger public.