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Related Experiment Video

Updated: May 21, 2026

Usability Evaluation of Augmented Reality: A Neuro-Information-Systems Study

Usability Evaluation of Augmented Reality: A Neuro-Information-Systems Study

Published on: November 30, 2022

An online brain-computer interface for detecting incongruity in augmented reality applications.

Michael Wimmer^1,2, Neven Elsayed¹, Bruce H Thomas³

¹Know Center Research GmbH, Graz, Austria.

Journal of Neural Engineering

|May 19, 2026

Summary

We developed a hybrid brain-computer interface to detect when augmented reality information mismatches user expectations. This system accurately identifies incongruities, improving user experience and trust in AR systems.

Keywords:

N400 augmented reality (AR)electroencephalography (EEG)event-related potential (ERP)eye tracking

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Last Updated: May 21, 2026

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Published on: November 30, 2022

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Published on: July 7, 2023

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Published on: May 23, 2019

Area of Science:

Human-Computer Interaction
Neuroscience
Augmented Reality

Background:

Augmented reality (AR) systems can present digital information overlaid on the real world.
Discrepancies between AR information and user expectations, caused by data errors or biases, can degrade user experience and trust.
Detecting these incongruities is crucial for reliable AR applications.

Purpose of the Study:

To propose and evaluate a hybrid brain-computer interface (BCI) for detecting inconsistencies between physical objects and digital information in AR.
To enhance user experience and system trustworthiness in AR environments.

Main Methods:

Two experiments were conducted: an offline study integrating eye-tracking and electroencephalography (EEG) for incongruity detection, followed by an online study assessing real-time feedback.
EEG signals were analyzed to identify neural correlates of incongruity.

Main Results:

Consistent electroencephalographic responses, specifically a centroparietal N400 effect, were observed for incongruent AR augmentations across both studies.
The system achieved an average balanced accuracy of 70% in distinguishing congruent from incongruent information during the online study.

Conclusions:

The study demonstrates the feasibility of online incongruity detection using hybrid BCIs in AR.
This capability enables autonomous system adaptation, such as adjusting information presentation or providing real-time contextual support to users.