When Brain Signals Steer Generative AI: Agency Beyond Causal Influence

Generative AI and brain–computer interfaces both weaken direct control, an established determinant of human agency. Models decide how the result takes shape; brain signals act without the user's command. How agency arises when both hold at once is unknown. Here we introduce $\alpha$-Canvas, a closed-loop system in which alpha activity steers the mood of images continually regenerated by a diffusion model. Twenty-five participants completed trials driven by their own alpha activity or by another participant's, unaware of which. Agency ratings sat at the scale midpoint and were statistically equivalent whether the signal was their own or another's, though most of those items rose when participants changed the mood by opening and closing their eyes. Throughout, participants endorsed how clearly they could tell what drove the image less than every item asking whether they drove it. In interviews they described steering images they would not call their own, citing the opacity of the signal and the model's dominant role. Together, our findings suggest that in a loop of this kind agency depends less on whose signal drove the image than on the legibility of the user's own contribution to an autonomous generative process. Legibility is thus a design choice: a brain-driven work can make the signal's contribution visible or leave authorship open, as this one does.