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

Types of Errors: Detection and Minimization

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Systematic Error: Methodological and Sampling Errors

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...

Contaminants and Errors

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...

Control Systems

Control Systems

Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...

You might also read

Related Articles

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

Sort by

Same author

Leveraging Smart Bed Technology to Detect COVID-19 Symptoms: Case Study.

JMIR AI·2025

Same author

Validating a smart bed against polysomnography for sleep apnea detection.

Scientific reports·2025

Same author

Sleep and cardiorespiratory function assessed by a smart bed over 10 weeks post COVID-19 infection.

Scientific reports·2025

Same author

Unobtrusive Skin Temperature Estimation on a Smart Bed.

Sensors (Basel, Switzerland)·2024

Same author

Fine tuned personalized machine learning models to detect insomnia risk based on data from a smart bed platform.

Frontiers in neurology·2024

Same author

Feasibility of Unobtrusively Estimating Blood Pressure Using Load Cells under the Legs of a Bed.

Sensors (Basel, Switzerland)·2024

Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 6, 2026

Errors as a Means of Reducing Impulsive Food Choice

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

Towards error-free interaction.

Tsvetomira Tsoneva¹, Jordi Bieger, Gary Garcia-Molina

¹Philips Research, HTC 34, 5656 AE Eindhoven, The Netherlands. tsvetomira.tsoneva@philips.com

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 25, 2010

Summary

This summary is machine-generated.

This study introduces a practical error detection algorithm using electroencephalogram (EEG) signals to enhance human-machine interaction (HMI). The algorithm leverages individual brain responses to machine errors for improved performance and usability.

More Related Videos

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Related Experiment Videos

Last Updated: Jun 6, 2026

Errors as a Means of Reducing Impulsive Food Choice

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Area of Science:

Neuroscience
Human-Computer Interaction
Signal Processing

Background:

Human-machine interaction (HMI) systems depend on pattern recognition algorithms with inherent limitations.
Error awareness mechanisms within the human brain offer potential for improving HMI performance and usability.

Purpose of the Study:

To design a practical error detection algorithm utilizing electroencephalogram (EEG) signals for integration into HMI systems.
To address requirements of real-time operation, customization, and convenience in HMI error detection.

Main Methods:

An experimental framework was developed to simulate machine errors.
EEG signals were analyzed to identify brain potentials associated with machine error processing.
A personalized error detection algorithm was implemented, optimizing electrode site combinations and requiring minimal calibration.

Main Results:

Brain potentials indicative of machine error processing were confirmed.
The algorithm demonstrated effective single-trial error detection across six subjects.
Area under the ROC curve performance ranged from 0.75 to 0.98, highlighting subject-specific efficacy.

Conclusions:

The study successfully implemented a subject-specific EEG-based error detection algorithm for HMI.
The algorithm shows promise for enhancing HMI by integrating human error awareness mechanisms.
The approach offers a practical solution for real-time, customizable, and convenient HMI error detection.