Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

An Interactive Constraint-Based Decision-Support Tool for Pharmaceutical Formulation Development.

Pharmaceutics·2026

Same author

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

Journal of neural engineering·2026

Same author

Turning motor intentions into words: an MRCP-based BCI speller for motor-impaired users enhanced by task-specific calibration.

Journal of neural engineering·2026

Same author

Opposing cortical forces: Alpha slowing and sensorimotor mu acceleration during motor-related BCI training.

PLoS computational biology·2026

Same author

Source localization of simulated neural signals in a cervical spinal cord model.

Journal of neural engineering·2026

Same author

A Non-Invasive, MRCP-Based BCI for Online Communication.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026

Same journal

Correction: A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.

Scientific reports·2026

Same journal

Poly(bromophenol blue)/CoSn(OH)<sub>6</sub> cubic particles modified pencil graphite electrode for electrochemical determination of diphenhydramine.

Scientific reports·2026

Same journal

Dietary Chlorella, Spirulina, and acidifier modulate jejunal cytokine-related gene expression in broiler chickens.

Scientific reports·2026

Same journal

Perceived physical activity barriers in university students: associations with fatigue and eating behaviours.

Scientific reports·2026

Same journal

Refuge limitation structures habitat use in agricultural landscapes: evidence from Sunda pangolins.

Scientific reports·2026

Same journal

Lightweight stateless transaction verification with outsourced witness updates for UTXO blockchains.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 28, 2025

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Published on: May 19, 2023

Multimodal decoding of error processing in a virtual reality flight simulation.

Michael Wimmer^1,2, Nicole Weidinger¹, Eduardo Veas^1,3

¹Know-Center GmbH, Graz, Austria.

Scientific Reports

|April 22, 2024

Summary

This summary is machine-generated.

This study shows that pupil size data can detect errors in virtual reality. Combining pupil data with electroencephalography (EEG) improves error detection, especially in wearable systems.

More Related Videos

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Published on: May 10, 2012

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

Related Experiment Videos

Last Updated: Jun 28, 2025

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Published on: May 19, 2023

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Published on: May 10, 2012

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

Area of Science:

Human-Computer Interaction
Neuroscience
Wearable Technology

Background:

Head-mounted displays (HMDs) enable collection of physiological data (gaze, pupil size, heart rate).
Interactions with HMDs can lead to errors and unexpected virtual environment changes.
Error processing has traditionally been studied using brain signals (EEG) only.

Purpose of the Study:

Investigate multimodal physiological data for decoding error processing.
Examine feasibility of decoding errors using only pupil size data.
Propose and evaluate a hybrid decoding approach combining EEG and pupillometric signals.

Main Methods:

Assessed pupil size data for error decoding.
Developed a hybrid decoding approach integrating electroencephalography (EEG) and pupillometry.
Evaluated hybrid approaches against EEG-only methods in a virtual reality flight simulation.

Main Results:

Classifiers trained with pupil size data successfully decoded errors above chance levels.
Hybrid EEG and pupillometric approaches showed improved performance over EEG-only decoders.
Performance gains were notable in reduced-channel setups, enhancing usability for practical applications.

Conclusions:

Pupil size data is a viable method for decoding error processing in HMD interactions.
Hybrid brain-computer interfaces (BCIs) combining EEG and pupillometry offer enhanced error detection.
Findings support the development of practical, wearable BCIs for improved human-system interaction.