Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Long-term Potentiation

Long-term Potentiation

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

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

Neuronal Communication

Neuronal Communication

Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...

Neuroplasticity

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.

Storage

Storage

A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze each...

You might also read

Related Articles

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

Sort by

Same author

Diffusion-augmented YOLO26-Swin cascaded framework with hybrid SHAP-CAM for autonomous power grid inspection.

Autonomous intelligent systems·2026

Same author

Dataset on bus mobility and environmental indicators from Rio de Janeiro.

Scientific data·2025

Same author

Deep Learning Approach for Automatic Heartbeat Classification.

Sensors (Basel, Switzerland)·2025

Same author

Audio-Based Engine Fault Diagnosis with Wavelet, Markov Blanket, ROCKET, and Optimized Machine Learning Classifiers.

Sensors (Basel, Switzerland)·2024

Same author

Random Convolutional Kernel Transform with Empirical Mode Decomposition for Classification of Insulators from Power Grid.

Sensors (Basel, Switzerland)·2024

Same author

Bilateral Stroke: A Case Report.

Cureus·2024

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 30, 2026

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Decoding Electroencephalography Signal Response by Stacking Ensemble Learning and Adaptive Differential Evolution.

Matheus Henrique Dal Molin Ribeiro^1,2, Ramon Gomes da Silva¹, José Henrique Kleinubing Larcher³

¹Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Paraná (PUCPR), R. Imaculada Conceição 1155, Curitiba 80215-901, PR, Brazil.

Sensors (Basel, Switzerland)

|August 26, 2023

Summary

This summary is machine-generated.

This study introduces a novel hybrid model, JADE-STACK, for analyzing complex electroencephalography (EEG) signals. The JADE-STACK model significantly improves nonlinear system identification and prediction accuracy for neural data.

Keywords:

differential evolution electroencephalography signal response machine learning nonlinear system identification time-series modeling

More Related Videos

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Related Experiment Videos

Last Updated: Jun 30, 2026

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Area of Science:

Neuroscience
Machine Learning
Signal Processing

Background:

Electroencephalography (EEG) signals present complex nonlinear dynamics, making accurate data modeling challenging due to noise and artifacts.
Traditional methods struggle with the inherent nonlinearities and variability in brain activity data.
Developing robust models is crucial for reliable neural data identification and prediction.

Purpose of the Study:

To propose a novel hybrid framework, JADE-STACK, for nonlinear system identification using EEG signals.
To enhance the accuracy and reliability of neural data modeling and prediction.
To evaluate the model's performance in decoding EEG signal responses to physical perturbations.

Main Methods:

A hybrid framework combining stacked generalization (STACK) ensemble learning with the Adaptive Differential Evolution (JADE) algorithm.
Training five base learners: eXtreme Gradient Boosting, Gaussian Process, LASSO, Multilayer Perceptron, and Support Vector Regression.
Utilizing JADE to optimize hyperparameters for the Cubist model, which integrates predictions from base learners.

Main Results:

The JADE-STACK model achieved high accuracy, explaining an average of 94.50% (1-step ahead) and 67.50% (3-steps ahead) of data variability.
Demonstrated significant improvement over existing methods, ranging from 0.6% to 161% for 1-step ahead and 43.34% for 3-steps ahead predictions.
Outperformed individual base learners and other state-of-the-art techniques in nonlinear system identification.

Conclusions:

The JADE-STACK model offers a powerful and accurate approach for nonlinear system identification in EEG data.
It provides a reliable alternative for analyzing complex neural signals and developing predictive models.
The framework's ability to handle nonlinearities and noise makes it suitable for advancing brain-computer interfaces and neurological research.