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

Heart rate variability analysis using electrocardiograms during cardio-ankle vascular index measurement shows good agreement with resting electrocardiogram-based analysis in patients with diabetes: a retrospective cross-sectional study.

Diabetology international·2026
Same author

Utterance-Style-Dependent Speaker Verification Using Emotional Embedding with Pretrained Models.

Sensors (Basel, Switzerland)·2025
Same author

White and Gray Matter Abnormality in Burning Mouth Syndrome Evaluated with Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine·2023
Same author

Effect of antipsychotic use by patients with schizophrenia on deceleration capacity and its relation to the corrected QT interval.

General hospital psychiatry·2023
Same author

Non-native acoustic modeling for mispronunciation verification based on language adversarial representation learning.

Neural networks : the official journal of the International Neural Network Society·2021
Same author

Structural connectivity changes in the cerebral pain matrix in burning mouth syndrome: a multi-shell, multi-tissue-constrained spherical deconvolution model analysis.

Neuroradiology·2021
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025
See all related articles

Related Experiment Video

Updated: Mar 13, 2026

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram
06:12

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Published on: March 13, 2018

11.2K

Improving Eye Motion Sequence Recognition Using Electrooculography Based on Context-Dependent HMM.

Fuming Fang1, Takahiro Shinozaki1, Yasuo Horiuchi2

  • 1Department of Information Processing, Tokyo Institute of Technology, Yokohama, Japan.

Computational Intelligence and Neuroscience
|October 25, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a context-dependent Hidden Markov Model (HMM) for electrooculography (EOG) to improve eye-controlled communication. The new method significantly reduces errors in recognizing sequential eye movements, enhancing usability for individuals with severe motor impairments.

More Related Videos

Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content
10:41

Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

Published on: May 26, 2018

7.4K
Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
05:54

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Published on: October 18, 2018

6.8K

Related Experiment Videos

Last Updated: Mar 13, 2026

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram
06:12

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Published on: March 13, 2018

11.2K
Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content
10:41

Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

Published on: May 26, 2018

7.4K
Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
05:54

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Published on: October 18, 2018

6.8K

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Human-Computer Interaction

Background:

  • Eye motion-based human-machine interfaces (HMIs) offer communication for individuals with severe motor impairments.
  • Electrooculography (EOG) detects eye movements for these HMIs, but accuracy is limited by sequential motion interference.
  • Current EOG recognition methods struggle with fast, sequential inputs due to inter-motion influence.

Purpose of the Study:

  • To develop an advanced EOG recognition model for faster and more accurate eye-based communication.
  • To address the challenge of recognizing sequential eye movements in EOG signals.
  • To investigate user adaptation techniques for optimizing HMI performance.

Main Methods:

  • Proposed a context-dependent Hidden Markov Model (HMM) approach for EOG signal processing.
  • Developed separate HMMs for identical eye motions based on their preceding context.
  • Introduced a user adaptation method utilizing a user-independent EOG model.

Main Results:

  • The context-dependent HMM significantly reduced the character error rate (CER) from 36.0% to 1.3% under user-dependent conditions.
  • Using context-dependent but user-independent HMMs resulted in a CER of 17.3%.
  • The proposed user adaptation method reduced the CER to 7.3% for user-independent models.

Conclusions:

  • Context-dependent HMMs provide superior accuracy for EOG-based communication by modeling adjacent eye motion influences.
  • User adaptation is effective in improving the performance of user-independent EOG models.
  • The developed approach enhances the feasibility of rapid and reliable eye-controlled communication systems.