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

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

You might also read

Related Articles

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

Sort by
Same author

Time to dispense with antiepileptic drug prophylaxis in brain tumor surgery?

Neuro-Chirurgie·2022
Same author

[Management of tumoral epilepsy in meningioma surgery: Review of the literature and survey of French national practices].

Neuro-Chirurgie·2019
Same author

Preoperative and intraoperative neurophysiological investigations for surgical resections in functional areas.

Neuro-Chirurgie·2017
Same author

[Caution and warning: About valproate and pregnancy].

L'Encephale·2016
Same author

Hippocampus and epilepsy: Findings from human tissues.

Revue neurologique·2015
Same author

Slow modulations of high-frequency activity (40-140-Hz) discriminate preictal changes in human focal epilepsy.

Scientific reports·2014

Related Experiment Video

Updated: Jul 20, 2026

Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
13:32

Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Published on: June 26, 2012

Artificial intelligence applied to electroencephalography in epilepsy.

C Alvarado-Rojas1, G Huberfeld2

  • 1School of Engineering, Pontificia Universidad Javeriana, Bogotá, Colombia.

Revue Neurologique
|March 30, 2025
PubMed
Summary

Artificial intelligence (AI) is revolutionizing epilepsy management by enhancing electroencephalography (EEG) analysis. AI algorithms offer improved efficiency in diagnosing epilepsy and monitoring patients, even with massive datasets from wearable devices.

Keywords:
Artificial intelligenceEEGEpilepsySeizure predictionSurgery

More Related Videos

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex
09:00

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

Published on: April 15, 2015

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
10:23

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

Related Experiment Videos

Last Updated: Jul 20, 2026

Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
13:32

Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Published on: June 26, 2012

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex
09:00

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

Published on: April 15, 2015

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
10:23

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

Area of Science:

  • Neurology
  • Medical Technology
  • Artificial Intelligence

Background:

  • Electroencephalography (EEG) has been used for over a century in epilepsy diagnosis but faces challenges with complex signals and artifact management.
  • Traditional visual interpretation of EEG data is becoming insufficient due to the exponential increase in data volume from wearable devices.
  • Artificial intelligence (AI) offers advanced capabilities for automated analysis, pattern recognition, and feature detection in complex biological signals.

Purpose of the Study:

  • To explore the fundamental principles of AI and its transformative potential in the field of EEG for epilepsy.
  • To discuss the implications and current limitations of AI in epilepsy diagnosis, treatment, and patient monitoring.
  • To highlight how AI is redefining the management of epilepsy through innovative approaches.

Main Methods:

  • Review of fundamental AI principles and their application to EEG signal analysis.
  • Discussion of AI's role in overcoming limitations of traditional EEG interpretation.
  • Exploration of AI-driven advancements in epilepsy diagnosis, treatment, and patient monitoring.

Main Results:

  • AI algorithms demonstrate superior capabilities in detecting specific EEG features and managing large datasets compared to human interpretation.
  • AI enhances efficiency in identifying subtle signal features and managing the increased data volume from wearable EEG devices.
  • AI shows potential for improving epilepsy diagnosis, treatment strategies, and patient monitoring, including seizure forecasting.

Conclusions:

  • AI is progressively transforming epilepsy care by enabling more efficient and sophisticated analysis of EEG data.
  • AI addresses the limitations of manual EEG interpretation, particularly with the rise of wearable technology.
  • AI is poised to redefine epilepsy management, offering innovative solutions for diagnosis, treatment, and patient monitoring, including seizure prediction.