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

Seizures: Classification01:13

Seizures: Classification

1.2K
Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
1.2K
Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

1.1K
Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Virtual Responsive Neurostimulation Implantation: From Intracranial Connectivity to Optimized Lead Placement.

medRxiv : the preprint server for health sciences·2026
Same author

Cortical stimulation reveals effective disconnection of the epileptogenic network at seizure onset.

Epilepsia·2026
Same author

Association between Interictal Spike Rate and Seizure Frequency in a Large Epilepsy Cohort.

medRxiv : the preprint server for health sciences·2026
Same author

The seizure embedding map: A spatio-temporal transformer for comparing patients by ictal intracranial EEG features at scale.

Journal of neural engineering·2026
Same author

Regional excitability, not epileptic pathology, drives stimulation-evoked interictal spike increases.

medRxiv : the preprint server for health sciences·2026
Same author

Targeted Connectomic Neuromodulation of the Orbitofrontal Cortex To Treat Obsessive-Compulsive Disorder.

medRxiv : the preprint server for health sciences·2026
Same journal

Ultra-flexible wireless endovascular stimulator for cortical simulation.

Journal of neural engineering·2026
Same journal

Influence of frequency and pulse train duration on respiratory responses during transcutaneous phrenic nerve stimulation in humans.

Journal of neural engineering·2026
Same journal

Dynamic functional graph-Laplacian priors integrated with optimization for EEG source localization.

Journal of neural engineering·2026
Same journal

Unveiling subject-specific causal latency in motor imagery: a physiologically transparent BCI via Riemannian tangent space fusion.

Journal of neural engineering·2026
Same journal

Cross-subject decoding of human neural data for speech Brain Computer Interfaces.

Journal of neural engineering·2026
Same journal

Cognitive and brain function enhancement in Gen X group after personalized, AI supervised EEG-neurofeedback training.

Journal of neural engineering·2026
See all related articles

Related Experiment Video

Updated: Dec 27, 2025

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.2K

Model-based design for seizure control by stimulation.

Arian Ashourvan1, Sérgio Pequito, Ankit N Khambhati

  • 1Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, United States of America. U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, United States of America.

Journal of Neural Engineering
|February 28, 2020
PubMed
Summary
This summary is machine-generated.

This study developed a model-based, control-theoretic strategy to terminate seizures. The approach uses mathematical modeling and intracranial EEG data to personalize interventions for epilepsy, improving closed-loop neurostimulation devices.

More Related Videos

Direct-current Stimulation and Multi-electrode Array Recording of Seizure-like Activity in Mice Brain Slice Preparation
09:39

Direct-current Stimulation and Multi-electrode Array Recording of Seizure-like Activity in Mice Brain Slice Preparation

Published on: June 7, 2016

11.0K
Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala
09:49

Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala

Published on: June 29, 2022

3.0K

Related Experiment Videos

Last Updated: Dec 27, 2025

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.2K
Direct-current Stimulation and Multi-electrode Array Recording of Seizure-like Activity in Mice Brain Slice Preparation
09:39

Direct-current Stimulation and Multi-electrode Array Recording of Seizure-like Activity in Mice Brain Slice Preparation

Published on: June 7, 2016

11.0K
Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala
09:49

Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala

Published on: June 29, 2022

3.0K

Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Epilepsy Research

Background:

  • Current brain stimulation methods for epilepsy are mostly empirical, limiting their effectiveness.
  • A need exists for theoretically grounded, model-based designs in closed-loop control strategies.

Purpose of the Study:

  • To develop and demonstrate a control-theoretic strategy for seizure termination.
  • To improve the effectiveness of closed-loop anti-epileptic devices through model-based designs.

Main Methods:

  • Utilized a dynamical systems approach to model seizures from intracranial EEG (iEEG) data of 94 patients.
  • Developed a control-theoretic strategy using static output feedback for linear time-invariant switching systems.
  • Validated the strategy in silico to dampen focal oscillatory sources with limited electrodes.

Main Results:

  • A single model form parsimoniously characterized seizure evolution across patients, despite unique patterns.
  • Individualized model parameters can inform tailored intervention strategies for localized seizure onset zones.
  • Seizure onset signifies a transition to a regime supporting prolonged rhythmic and focal activity.

Conclusions:

  • An integrative, model-based approach can inform the development of effective neurostimulation control algorithms.
  • This strategy has the potential to enhance implantable, closed-loop anti-epileptic devices.
  • Theoretical grounding in control theory offers a path to improve upon empirical brain stimulation paradigms.