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

Evaluating haptic experience using EEG and deep learning across multiple modalities: linking stimulus and self-reports.

Frontiers in neuroscience·2026
Same author

Improving Sleep Quality and Quantity in Hospitalized Patients With Melatonin: A Quality Improvement Project at HCA (Hospital Corporation of America) Oak Hill Hospital.

Cureus·2026
Same author

Recurrent, Acute Limb Ischemia Secondary to Arterial Thrombosis: A Devastating Complication in the Setting of Severe COVID-19 Infection.

HCA healthcare journal of medicine·2025
Same author

Centroparietal Alpha/Beta Asymmetry in Response to Urgency Elicited by Upper Body Vibration.

IEEE transactions on haptics·2025
Same author

Analyzing handwriting legibility through hand kinematics.

Frontiers in artificial intelligence·2025
Same author

BandFocusNet: A Lightweight Model for Motor Imagery Classification of a Supernumerary Thumb in Virtual Reality.

IEEE open journal of engineering in medicine and biology·2025

Related Experiment Video

Updated: Aug 25, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.5K

An ensemble deep learning approach to evaluate haptic delay from a single trial EEG data.

Haneen Alsuradi1,2, Mohamad Eid1

  • 1Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.

Frontiers in Robotics and AI
|October 14, 2022
PubMed
Summary

This study uses deep learning to detect haptic delay in human-computer interaction from brain activity. The model accurately identifies haptic delay and its severity, paving the way for better user experience evaluation.

Keywords:
CNNEEGconvolutional neural networkdeep learninghapticsneurohapticswavelet transform

More Related Videos

Combined Shuttle-Box Training with Electrophysiological Cortex Recording and Stimulation as a Tool to Study Perception and Learning
08:43

Combined Shuttle-Box Training with Electrophysiological Cortex Recording and Stimulation as a Tool to Study Perception and Learning

Published on: October 22, 2015

10.4K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K

Related Experiment Videos

Last Updated: Aug 25, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.5K
Combined Shuttle-Box Training with Electrophysiological Cortex Recording and Stimulation as a Tool to Study Perception and Learning
08:43

Combined Shuttle-Box Training with Electrophysiological Cortex Recording and Stimulation as a Tool to Study Perception and Learning

Published on: October 22, 2015

10.4K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K

Area of Science:

  • Human-Computer Interaction
  • Neuroscience
  • Machine Learning

Background:

  • Haptic technologies enhance interaction with virtual environments, but network delays in tele-haptic systems disrupt synchrony with other senses.
  • Delayed haptic feedback negatively impacts user performance and experience, yet its neural correlates and implications remain understudied.
  • Deep learning offers potential for interpreting brain activity in response to haptic experiences, including delays.

Purpose of the Study:

  • To develop a deep learning model for detecting the presence and severity of haptic delay using Electroencephalography (EEG) data.
  • To investigate the neural responses to haptic delays in visuo-haptic interaction tasks.
  • To establish a method for cognitive evaluation of user experience with haptic interfaces.

Main Methods:

  • An ensemble model combining 2D Convolutional Neural Networks (CNN) and transformers was proposed to analyze single-trial EEG data.
  • Two experiments were conducted: one for detecting haptic delay presence in a discrete force feedback task (bouncing ball simulation) and another for classifying delay severity in a continuous force feedback task (object grasping).
  • EEG data, including raw signals and their wavelet transforms, were used for model training and validation.

Main Results:

  • The ensemble model achieved high accuracy (0.9142 ± 0.0157) in detecting the presence of haptic delay during discrete force feedback.
  • The model demonstrated moderate performance (0.6625 ± 0.0067) in classifying the severity of haptic delay (four levels) during continuous force feedback.
  • Model performance was evaluated using raw EEG data and wavelet transforms with various wavelet kernels.

Conclusions:

  • The proposed deep learning approach effectively detects haptic delay from EEG, offering a novel method for cognitive evaluation of user experience in haptic systems.
  • This research contributes to understanding the neural impact of haptic delays, crucial for designing more responsive and immersive human-computer interactions.
  • The findings represent a significant step towards real-time, brain-based assessment of user experience in advanced haptic interface applications.