This approach could make BCIs more intuitive and useful for a wider range of people. Advances in neural recording and machine learning mean we can now detect patterns that correspond to imagined sights, sounds, or outcomes rather than only motor cortex spikes. Designing systems around anticipated sensory results may reduce training time, allow users to control devices with fewer constraints, and make assistive technology more adaptable across tasks and bodies.
If BCIs learn to read the “what for” of actions, they could expand human potential in practical ways: restoring communication, enabling creative expression, or opening new tools for collaboration. The article invites a look at how sensory-based decoding might be implemented and what ethical and technical questions follow. Follow the link to explore how this idea could reshape the next generation of brain-driven technology.
Brain–computer interface (BCI) research has achieved remarkable technical progress but remains limited in scope, typically relying on motor and visual cortex signals in limited patient populations. We propose a paradigm shift in BCI design rooted in ideomotor theory, which conceptualizes voluntary action as driven by internally represented sensory outcomes. This underused framework offers a principled basis for next-generation BCIs that align closely with the brain’s natural intentional and action-planning architecture. We suggest a more intuitive, generalizable, and scalable path by reorienting BCIs around the ‘what for’ of action—user goals and anticipated effects. This shift is timely and feasible, enabled by advances in neural recording and artificial intelligence–based decoding of sensory representations. It may help resolve challenges of usability and generalizability in BCI design.
