March, 2021

Our research on brain-computer interfacing for modeling personal attractiveness was featured in the Independent, the Daily Mail, VICE, RT, and many others.

February, 2021

Our paper titled “Brain-computer interface for generating personally attractive images” has been accepted for publication by IEEE Transactions on Affective Computing. Watch out for a preprint.

January, 2021

Our paper titled “Collaborative Filtering with Preferences Inferred from Brain Signals” has been accepted for publication at The Web Conference (WWW). Watch out for a preprint.

Our paper titled “Spoken Conversational Context Improves Query Auto Completion in Web Search” has been accepted for publication in ACM Transactions on Information Systems. Watch out for a preprint.

December, 2020

Our research was reviewed by the Cognitive Neuroscience Society, see here.

October, 2020

Our research on neuroadaptive generative modeling was featured in the Independent,, La Vanguardia, ACM and many others.

September, 2020

The group starts to operate also at University of Copenhagen, Denmark. We are looking forward for new collaborations and exciting research!

Our paper on connecting generative adversarial networks (GANs) with brain-computer interfaces, titled Neuroadaptive modelling for generating images matching perceptual categories is published by Scientific Reports (Nature). The paper is available here.

August, 2020

Our paper titled “Generating Images Instead of Retrieving Them: Relevance Feedback on Generative Adversarial Networks” published at SIGIR 2020

The paper is available here

May, 2020

Our YouTube channel is now available here.

Two of our papers, published in Journal of the Association for Information Science and Technology, receive a high-impact award being in the top 10% most downloaded papers! The papers are: Integrating neurophysiologic relevance feedback in intent modeling for information retrieval and Understanding user behavior in naturalistic information search tasks.

April, 2020

Our paper on interactive GANs accepted for publication at SIGIR 2020. Stay tuned for a preprint.

Our paper on brain responses and information theory, titled "Information gain modulates brain activity evoked by reading" accepted for publication at Scientific Reports (Nature). The paper is available here.

February, 2020

Our paper on brainsourcing, titled "Brainsourcing: Crowdsourcing Recognition Tasks via Collaborative Brain-Computer Interfacing" accepted for publication at CHI 2020. Paper available here.


What if computing systems could sense thoughts and cognitive states in response to perception of our environment? What if computing systems could assist us by augmenting our cognitive capabilities, retrieving and recommending information, and monitoring our well-being? What if we could interact with computing directly via passive observations of our physiology and brain activity? Our research focuses on cognitive computing with a vision of using machine learning to interpret multitude of signals originating from human cognition, and using signals measured from human cognition to augment computation. Our research approach is multidisciplinary and combines methods from several subfields of computer science, electrical engineering and signal processing, and cognitive neuroscience. We apply the developed methods and methodologies in brain-computer interfacing, physiological computing, information retrieval and recommender systems, user modeling, and human-computer interaction. The group operates at University of Helsinki, Finland and University of Copenhagen, Denmark. Our research is funded by the Academy of Finland and supported by the Helsinki Institute for Information Technology HIIT and the Finnish Center for Artificial Intelligence (FCAI).

Research highlights

Brain-computer Interfacing

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.

Interactive Information Retrieval

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.

Cognitive Modeling

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.


Our research is published in top venues for information management, processing, and retrieval (such as SIGIR, TOIS, JASIST and IPM) and human-computer interaction (such as CHI, UIST, and IUI), as well as general science outlets (such as Communications of the ACM and Scientific Reports). Below is a selection of recent publications from our group that represent different research directions, from brain-computer interfacing and cognitive modeling, to interactive information generation and retrieval.

A good approximation of our publications is also provided by Google Scholar


Group members

Tuukka Ruotsalo
Group leader

Michiel Spapé
Senior researcher
(now officially with Niklas Ravaja's group)

Tung Vuong
PhD student
(supervised with G. Jacucci)

Keith Davis
PhD student

Carlos de la Torre Ortiz
PhD student

Jun Ma
PhD student

Zania Sovijärvi-Spapé
Research Assistant
(Interaction laboratory), on leave


  • Lauri Kangassalo (moved to FMI)
  • Oswald Barral (co-supervised with G. Jacucci, moved to The University of British Columbia)
  • Khalil Klouche (co-supervised with G. Jacucci, founded
  • Payel Bandyopadhyay (co-supervised with G. Jacucci, moved to Texas A&M University)
  • Ksenia Konyushkova (moved to EPFL and DeepMind)
  • Miamaria Saastamoinen (moved to National Library of Finland)
  • Antti Lipsanen (moved to Etsimo Healthcare)
  • Sean Weber (moved to Etsimo Healthcare)
  • Jukka Leino (moved to TuTasa)
  • Kseniia Belorustceva (moved to Digital Workforce)
  • Work with us

    We are hiring! Open PhD positions in self-supervised learning, brain-computer interfacing and machine learning for neuroimaging data. In case you are a student at University of Helsinki or University of Copenhagen and are looking for Master’s thesis position that could be continued as a PhD, this is also possible. Please contact Tuukka for more information.
    We are also hiring via FCAI/HICT call (project 29)