Framework for Electroencephalography-based Evaluation of User Experience

TL;DR

This paper presents a framework using EEG to continuously evaluate user mental states like workload, attention, and error recognition, enhancing understanding of user experience in interactive systems.

Contribution

It introduces novel methods for real-time EEG-based assessment of mental workload, attention, and error detection during interaction tasks, validated in virtual environments.

Findings

EEG measures effectively differentiate interaction techniques.

The framework can compare usability of devices like keyboards and touch interfaces.

EEG-based evaluation improves usability assessment of complex systems.

Abstract

Measuring brain activity with electroencephalography (EEG) is mature enough to assess mental states. Combined with existing methods, such tool can be used to strengthen the understanding of user experience. We contribute a set of methods to estimate continuously the user's mental workload, attention and recognition of interaction errors during different interaction tasks. We validate these measures on a controlled virtual environment and show how they can be used to compare different interaction techniques or devices, by comparing here a keyboard and a touch-based interface. Thanks to such a framework, EEG becomes a promising method to improve the overall usability of complex computer systems.