fNIRS presents an opportunity to measure brain data in more real-world contexts and is increasingly popular in HCI. Yet classifying levels of mental workload from fNIRS data is a long way behind the machine learning of more established types of brain data (e.g. computer vision or text). This lack of established processes means that fNIRS ML papers often make critical mistakes or omit crucial details for using them as benchmarks in future research. The aim of this project is to develop new tools and benchmarks to further the reliability of classifying cognitive states using fNIRS data.

HttF 2019 paper

Frontiers paper

fNIRS tools

Benchmarking framework

For more information please contact:
Johann Benerradi
University of Nottingham
johann.benerradi@nottingham.ac.uk

Classifying cognitive activity from physiological data is increasingly a machine learning problem (see our other project!) so we are also focusing on the future questions: how will people track their cognitive activity data as cognitive form of personal informatics? and how will behaviour change? We've been starting the discussions in this area within the HCI community.

CHI2022 Paper

Find out more, and get involved!

For more information please contact:
Max L. Wilson
University of Nottingham
max.wilson@nottingham.ac.uk

Consumer Nueurotechnology is arriving, and even being adopted in the workplace. So what concerns do people have? And what ethical concerns will we face? This project is focused on understanding the ethical and moral concerns using a range of techniques, including the use of the Moral IT cards to provoke discussions.

FAccT2022 Paper

CHIWORK'22 Paper

Sustainability Article

For more information please contact:
Max L. Wilson
University of Nottingham
max.wilson@nottingham.ac.uk

Many forms of work are becoming more cognitively challenging than physically challenging, from office work to manufacturing contexts. Our EPSRC funded research has focused on evaluating how people work and what physiological data says about their cognitive activity.

Journal Article: Office Work

Journal Article: Manufacturing Work

Journal Article: Workload Alerts

DigiTOP website (EPSRC)

For more information please contact:
Max L. Wilson
University of Nottingham
max.wilson@nottingham.ac.uk

Human-Robot Interaction is challenging and complex, if not also dangerous. A key challenge is for the robots to sense and understand the capability and e.g. current mental workload of the user. Whether in manufacturing or other industries, this project aims to a) understand the cognitive challenges involved in working with robots, and b) understand how to adapt the controls and actions of the robot based on the state of the user.

CHI'22 Abstract

For more information please contact:
Horia Maior
University of Nottingham
horia.maior@nottingham.ac.uk

Virtual Reality and Augmented Reality have many exciting applications from treatments to museums to gameful experiences. In many cases, we want these experiences to adapt to the user. This project is focused on understanding how we can measure mental workload within mixed reality experiences to a) evaluate task performance or b) utilise the data in adaptive entertainment.

Exposure Therapy Article

For more information please contact:
Aleksandra Landowska
University of Nottingham
aleksandra.landowska@nottingham.ac.uk

This research project has studied how brain data can be used within adaptive movie experiences to allow a form of lean-back interaction rather than disrupting a film experience with requiring explicit action and cognitive thought.

HCI Article on Control

CHI2019 Paper

CHI2016 Paper

Book Chapter on Brain-Controlled Art

For more information please contact:
Richard Ramchurn
University of Nottingham
richard.ramchurn@nottingham.ac.uk

Our earliest team work was on understanding how taking fNIRS measurements in user studies would work in practice, and what aspects of HCI study practices would affect measurements. Our work demonstrated the kinds of activities that create noise in the data, and that think aloud protocols can be used in parallel.

fNIRS with Think Aloud Method (CHI2014)

HCI artefacts and data noise (CHI2015)

fNIRS in usability studies (CHI2016)

For more information please contact:
Horia Maior
University of Nottingham
horia.maior@nottingham.ac.uk