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Current image semantic editing approaches rely on manual annotations or use unsupervised techniques that require a human to assess semantic relevance. We propose a novel paradigm in which we measure implicit responses direcly from the brain (EEG) to detect feature saliency and use them for image editing.

See the external project page. The paper presents a novel model-based method for intelligent tutoring, with particular emphasis on the problem of selecting teaching interventions in interaction with humans. Whereas previous work has focused on either personalization of teaching or optimization of teaching intervention sequences, the proposed individualized model-based planning approach represents convergence of these two lines of research.

We present the first of its kind interactive brain-computer interface for image generation combining generative adversarial neural networks and brain feedback. We demonstrate the technique with realistic tasks (such as generate a blond face), and complex combinations of the tasks (such as generate an old female face that is not smiling), and show that the technique can generate images matching user intentions.