Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Rare twin cysteine residues in the HIV-1 envelope variable region 1 link to neutralization escape and breadth development.

Cell host & microbe·2026
Same author

Nitrite binding modes in ferric heme proteins probed by HYSCORE spectroscopy.

Dalton transactions (Cambridge, England : 2003)·2026
Same author

Intimate Partner Violence Victimization and HIV PrEP Adherence Among Gay, Bisexual Men, and Other Men Who Have Sex with Men in Ukraine.

AIDS and behavior·2026
Same author

Seizure forecasting with multiple timescales and features.

Epilepsia·2026
Same author

Textured foot orthotics and proprioception: augmenting cutaneous feedback to improve joint position sense accuracy.

Somatosensory & motor research·2025
Same author

Demonstration of Advanced Timing Schemes in Time-Resolved X‑ray Diffraction Measurements.

ACS omega·2025

Related Experiment Video

Updated: Dec 20, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.1K

Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning.

Daniel Schmidt1,2, Javier Villalba Diez1,3,4, Joaquín Ordieres-Meré1

  • 1Department of Business Intelligence, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, 28006 Madrid, Spain.

Sensors (Basel, Switzerland)
|May 24, 2020
PubMed
Summary
This summary is machine-generated.

This study used electroencephalography (EEG) to analyze brain activity during shopfloor management (SM) system use. Deep learning identified distinct neurological patterns, differentiating between goal-oriented and continuous improvement strategies for Industry 4.0.

Keywords:
EEG sensorsdeep learningmachine learningmanufacturing systemsshopfloor management

More Related Videos

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

3.0K
Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

13.0K

Related Experiment Videos

Last Updated: Dec 20, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.1K
A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

3.0K
Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

13.0K

Area of Science:

  • Industrial Engineering
  • Neuroscience
  • Cognitive Science

Background:

  • Industry 4.0 transformation requires active shopfloor integration.
  • Shopfloor management (SM) systems are crucial for this integration.
  • Existing SM systems fall into two main categories: goal-fixed (e.g., Balanced Scorecard) and continuous improvement-focused (e.g., Hoshin Kanri Tree).

Purpose of the Study:

  • To differentiate between distinct shopfloor management (SM) systems by analyzing workers' neurological patterns.
  • To evaluate the advantages and disadvantages of different SM approaches through brain activity analysis.
  • To provide insights for Industry 4.0 leaders in selecting appropriate SM systems.

Main Methods:

  • Utilized non-invasive electroencephalography (EEG) sensors to capture brain electrical activity.
  • Employed a deep learning (DL) soft sensor for classifying recorded EEG data.
  • Analyzed correlations within EEG signals to identify brain activity characteristics.

Main Results:

  • Achieved 96.5% accuracy in classifying EEG data using the DL soft sensor.
  • Detected significant differences and relevant characteristics in brain activity patterns corresponding to different SM systems.
  • Demonstrated the feasibility of using neurophysiological data to distinguish between SM methodologies.

Conclusions:

  • Neurological pattern analysis via EEG can effectively differentiate between various shopfloor management systems.
  • Findings offer a novel method for assessing SM system effectiveness and guiding Industry 4.0 implementation.
  • This research bridges neuroscience and industrial engineering to optimize shopfloor strategies.