Present Brain-Computer Interfacing (BCI) technology allows inference and detection of cognitive and affective states, but fairly little has been done to study scenarios in which such information can facilitate new applications that rely on modeling human cognition. One state that can be quantified from various physiological signals is attention. Estimates of human attention can be used to reveal preferences and novel dimensions of user experience. Previous approaches have tackled these incredibly challenging tasks using a variety of behavioral signals, from dwell-time to click-through data, and computational models of visual correspondence to these behavioral signals. However, behavioral signals are only rough estimations of the real underlying attention and affective preferences of the users. Indeed, users may attend to some content simply because it is salient, but not because it is really interesting, or simply because it is outrageous. With this paper, we put forward a research agenda and example work using BCI to infer users' preferences, their attentional correlates towards visual content, and their associations with affective experience. Subsequently, we link these to relevant applications, such as information retrieval, personalized steering of generative models, and crowdsourcing population estimates of affective experiences.

Soon, computers could sense that users have a problem and come to the rescue. This is one of the possible implications of new research at University of Copenhagen and University of Helsinki. "We can make a computer edit images entirely based on thoughts generated by human subjects. The computer has absolutely no prior information about which features it is supposed to edit or how. Nobody has ever done this before," says Associate Professor Tuukka Ruotsalo, Department of Computer Science, University of Copenhagen.

Soon, we won't need to use the Help function. The computer will sense that we have a problem and come to the rescue by itself. This is one of the possible implications of new research at University of Copenhagen and University of Helsinki. "We can make a computer edit images entirely based on thoughts generated by human subjects. The computer has absolutely no prior information about which features it is supposed to edit or how. Nobody has ever done this before," says Associate Professor Tuukka Ruotsalo, Department of Computer Science, University of Copenhagen.

Soon, we won't need to use the Help function. The computer will sense that we have a problem and come to the rescue by itself. This is one of the possible implications of new research at University of Copenhagen and University of Helsinki. "We can make a computer edit images entirely based on thoughts generated by human subjects. The computer has absolutely no prior information about which features it is supposed to edit or how. Nobody has ever done this before," says Associate Professor Tuukka Ruotsalo, Department of Computer Science, University of Copenhagen.

Summary: Combining EEG brain function data, brain-computer interface technology, and artificial intelligence, researchers have created a system that can generate an image of what a person is thinking. Researchers at the University of Helsinki have developed a technique in which a computer models visual perception by monitoring human brain signals. In a way, it is as if the computer tries to imagine what a human is thinking about. As a result of this imagining, the computer is able to produce entirely new information, such as fictional images that were never before seen. The technique is based on a novel brain-computer interface.

At each identical desk, there is a computer with a person sitting in front of it playing a simple identification game. The game asks the user to complete an assortment of basic recognition tasks, such as choosing which photo out of a series that shows someone smiling or depicts a person with dark hair or wearing glasses. The player must make their decision before moving onto the next picture. Only they don't do it by clicking with their mouse or tapping a touchscreen. Instead, they select the right answer simply by thinking it.