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

A novel backpropagation algorithm based on negated kurtosis loss for training shallow, convolutional, and deep neural networks.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Delta-band cortical speech tracking predicts audiovisual speech-in-noise benefit from natural and simplified visual cues.

NeuroImage·2025
Same author

How to Evaluate Signal Quality of ear-ECG?

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

Impact of Pulmonary Vein Isolation on Atrial Fibrillation Organisation: Correlation of Intracardiac and Surface Electrocardiogram Measures.

Journal of cardiovascular electrophysiology·2025
Same author

UAdam: Unified Adam-Type Algorithmic Framework for Nonconvex Optimization.

Neural computation·2024
Same author

From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People.

IEEE journal of translational engineering in health and medicine·2024

Related Experiment Video

Updated: Aug 12, 2025

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

2.7K

Ear-EEG sensitivity modeling for neural sources and ocular artifacts.

Metin C Yarici1, Mike Thornton1, Danilo P Mandic1

  • 1Communications and Signal Processing, Electronic and Electrical Engineering, Imperial College, London, United Kingdom.

Frontiers in Neuroscience
|January 26, 2023
PubMed
Summary

Ear-electroencephalography (ear-EEG) effectively monitors brain activity, especially from temporal lobes. This study establishes ear-EEG sensitivity to neural and ocular artifact sources, supporting its use in wearable brain monitoring.

Keywords:
EEG artifactsblinksear-EEGforward modelinghorizontal saccadesneural sourcesvertical saccades

More Related Videos

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

43.4K
Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
09:25

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

7.0K

Related Experiment Videos

Last Updated: Aug 12, 2025

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

2.7K
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

43.4K
Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
09:25

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

7.0K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Wearable Technology

Background:

  • The ear-electroencephalography (ear-EEG) is a developing technology for wearable brain monitoring.
  • Existing research validates ear-EEG for specific applications, but its sensitivity to diverse neural sources and artifacts remains uncharacterized.
  • A comprehensive understanding of the source-sensor relationship is crucial for advancing ear-EEG applications.

Purpose of the Study:

  • To establish the source-sensor relationship for neural activity across the brain surface using ear-EEG.
  • To model the sensitivity of ear-EEG configurations to ocular artifact sources.
  • To provide a foundation for the integration of ear-EEG into conventional electroencephalography (EEG) paradigms.

Main Methods:

  • Utilized volume conductor modeling to simulate ear-EEG sensitivity.
  • Investigated sensitivity to a range of neural sources across the brain.
  • Modeled sensitivity to ocular artifacts, including blinks and saccades.

Main Results:

  • Ear-EEG configurations demonstrate significant sensitivity to neural sources originating from the temporal lobes.
  • The study quantifies ear-EEG sensitivity to neural sources located further from the temporal regions.
  • Proportional scaling of ocular artifacts and neural signals was observed across various ear-EEG setups, indicating potential for artifact management.

Conclusions:

  • Ear-EEG is suitable for monitoring neural activity from the temporal lobes and can detect signals from more distant brain regions.
  • The findings support the use of ear-EEG in environments with potential ocular artifacts.
  • This research provides crucial data to guide current and future experimental ear-EEG studies and applications.