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

Associations among sleep quality, cognitive decline, and Alzheimer's disease pathology in older adults: A longitudinal study.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Combining plasma biomarkers and cognitive challenge tests enhances prediction of functional trajectories of decline among older adults with cognitive impairment.

Journal of Alzheimer's disease : JAD·2026
Same author

Factors associated with discordant visual and quantitative amyloid PET results.

Alzheimer's & dementia (Amsterdam, Netherlands)·2026
Same author

Semantic intrusion errors differentiate between amnestic MCI who are plasma p-tau<sub>217</sub>+ from p-tau<sub>217</sub>- after adjusting for initial learning strength.

Frontiers in neurology·2025
Same author

Failure to recover from proactive semantic interference predicts trajectory of decline in everyday function among older adults with amnestic mild cognitive impairment.

International psychogeriatrics·2025
Same author

Spike isolation from background signal in neonatal EEG data using an integrated independent component analysis method.

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

Related Experiment Video

Updated: Jun 19, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

A comparative study of intracranial EEG files using nonlinear classification methods.

Maria Tito1, Mercedes Cabrerizo, Melvin Ayala

  • 1Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, 10555 W. Flagler Street, Miami, FL 33174, USA.

Annals of Biomedical Engineering
|October 20, 2009
PubMed
Summary
This summary is machine-generated.

This study highlights gamma frequencies (36-44 Hz) for effective seizure detection using a novel 2D decision plane. Nonlinear classification methods achieved high accuracy, sensitivity, and specificity in analyzing electroencephalogram (EEG) data.

More Related Videos

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Related Experiment Videos

Last Updated: Jun 19, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Area of Science:

  • Neuroscience
  • Signal Processing
  • Machine Learning

Background:

  • Epilepsy diagnosis relies on accurate seizure detection from electroencephalogram (EEG) data.
  • Traditional methods often struggle with the complexity of EEG signals.
  • Nonlinear classification offers potential for improved seizure detection accuracy.

Purpose of the Study:

  • To comparatively evaluate nonlinear classification methods against support vector machines for seizure detection.
  • To identify optimal frequency bands and features for seizure detection using subdural EEG data.
  • To develop and validate a novel 2D decision plane for enhanced seizure detection.

Main Methods:

  • Comparative analysis of nonlinear classification methods and support vector machines.
  • Feature extraction from subdural EEG data across frequency bands (1-44 Hz).
  • Development of a 2D decision plane using seizure duration and gamma frequency components (36-44 Hz).
  • Evaluation of 157 intracranial EEG files from 14 patients using spectral power analysis.

Main Results:

  • Gamma frequencies (36-44 Hz) demonstrated the highest suitability for seizure detection.
  • A 2D decision plane using seizure duration and maximum gamma frequency components proved effective.
  • Nonlinear decision functions with a nonlinearity degree of three yielded optimal results.
  • Achieved high performance metrics: 96.3% sensitivity, 96.8% specificity, and 96.7% accuracy.

Conclusions:

  • Nonlinear classification methods, particularly with gamma frequencies and a 2D decision plane, offer a robust approach to seizure detection.
  • The proposed 2D decision plane provides a unique and effective framework for analyzing seizure characteristics.
  • This method shows significant promise for improving the accuracy and reliability of automated seizure detection systems.