We conduct fundamental research into developing new types of implicit brain-computer interfaces and applying them in novel, adaptive and interactive systems. Our current interaction with information retrieval systems rely on explicit interaction. Could we mine the relevance to or interest of the user directly from the human mind? Our research shows, for the first time, that with the help of EEG interpreted via machine learning is indeed possible.
We develop interactive machine learning methods for cognitive modeling of information retrieval, visualization, and information generation, and systems implementing these in real-world information seeking applications. We have developed a technique called interactive intent modeling that allows humans to direct exploratory search: the technique has been implemented in a real-world search engine SciNet.
We develop machine learning models to model, understand, and decode cognitive states of humans interacting with large information spaces, such as texts, images, and video. We have discovered that human information processing of language stimuli when reading follows information theoretic principles and can be explained by information gain.
Tuukka Ruotsalo
Group leader
Michiel Spapé
Senior researcher
(now officially with Niklas Ravaja's group)
Tung Vuong
Post Graduate Researcher
(supervised with G. Jacucci)
Keith Davis
Post Graduate Researcher
Carlos de la Torre Ortiz
Research Assistant
Zania Sovijärvi-Spapé
Research Assistant
(Interaction laboratory), on leave