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

Long-Term Memory01:18

Long-Term Memory

663
Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...
663
Convolution Properties II01:17

Convolution Properties II

582
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
582
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Convolution Properties I01:20

Convolution Properties I

562
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
562
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
Long-term Depression01:05

Long-term Depression

33.2K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
33.2K

You might also read

Related Articles

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

Sort by
Same author

Thalamocortical seizure onset patterns in drug-resistant focal epilepsy.

Brain communications·2026
Same author

The Need for Demonstrated Clinical Translational Evidence in Submissions to the IEEE Journal of Translational Engineering in Health and Medicine.

IEEE journal of translational engineering in health and medicine·2026
Same author

Widening participation in the International League Against Epilepsy: Looking to the future.

Epilepsia open·2026
Same author

Differentiating long QT syndrome genotypes using electrocardiographic geometric parameterization and machine learning approaches.

Biomedical physics & engineering express·2026
Same author

M<sup>2</sup>NuFFT-A computationally efficient suboptimal power spectrum estimator for fast exploration of nonuniformly sampled time series.

Digital signal processing·2026
Same author

Comparison of Pulse Wave Arrival Times Measured by Bioimpedance Method and Doppler Ultrasound.

Physiological research·2026

Related Experiment Video

Updated: Jan 21, 2026

C. elegans Positive Butanone Learning, Short-term, and Long-term Associative Memory Assays
09:58

C. elegans Positive Butanone Learning, Short-term, and Long-term Associative Memory Assays

Published on: March 11, 2011

30.4K

Exploiting Graphoelements and Convolutional Neural Networks with Long Short Term Memory for Classification of the

P Nejedly1,2,3, V Kremen4,5,6, V Sladky7,8

  • 1Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, 55905, USA. Nejedly.Petr@mayo.edu.

Scientific Reports
|August 8, 2019
PubMed
Summary

This study introduces an interpretable deep learning model for classifying electroencephalogram (EEG) signals. The novel approach uses convolutional neural networks (CNN) with long short-term memory (LSTM) to visualize and understand EEG classifications.

More Related Videos

Short-Term Free-Floating Slice Cultures from the Adult Human Brain
09:14

Short-Term Free-Floating Slice Cultures from the Adult Human Brain

Published on: November 5, 2019

9.6K
Isolation Method for Long-Term and Short-Term Hematopoietic Stem Cells
06:41

Isolation Method for Long-Term and Short-Term Hematopoietic Stem Cells

Published on: May 19, 2023

2.5K

Related Experiment Videos

Last Updated: Jan 21, 2026

C. elegans Positive Butanone Learning, Short-term, and Long-term Associative Memory Assays
09:58

C. elegans Positive Butanone Learning, Short-term, and Long-term Associative Memory Assays

Published on: March 11, 2011

30.4K
Short-Term Free-Floating Slice Cultures from the Adult Human Brain
09:14

Short-Term Free-Floating Slice Cultures from the Adult Human Brain

Published on: November 5, 2019

9.6K
Isolation Method for Long-Term and Short-Term Hematopoietic Stem Cells
06:41

Isolation Method for Long-Term and Short-Term Hematopoietic Stem Cells

Published on: May 19, 2023

2.5K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Machine Learning

Background:

  • Electroencephalogram (EEG) classification traditionally relies on expert visual review.
  • Automated EEG classification using machine learning, particularly convolutional neural networks (CNNs), shows promise but lacks interpretability.
  • Understanding the electrophysiological basis of CNN decisions in EEG analysis is crucial for clinical and research applications.

Purpose of the Study:

  • To develop an interpretable CNN architecture for classifying intracranial EEG (iEEG) signals.
  • To enable visualization of iEEG components driving classification decisions (normal, pathological, artifactual).
  • To bridge the gap between machine learning model outputs and electrophysiological interpretation.

Main Methods:

  • Proposed a hybrid CNN architecture integrating long short-term memory (LSTM) networks.
  • Developed a classification heatmap to visualize the contribution of iEEG graphoelements to the model's output.
  • Applied the model to iEEG data for classification of brain activity.

Main Results:

  • The proposed CNN-LSTM model successfully classified iEEG activity as normal, pathological, or artifactual.
  • The classification heatmap effectively visualized the specific iEEG transients influencing the classification outcome.
  • The interpretability feature allows for electrophysiological validation of the model's decisions.

Conclusions:

  • The developed interpretable CNN-LSTM model offers a transparent approach to automated EEG classification.
  • Visualization of iEEG graphoelements enhances trust and understanding of machine learning-based neurological assessments.
  • This method facilitates the integration of advanced AI tools into clinical neurology and neurophysiological research.