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

You might also read

Related Articles

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

Sort by

Same author

A systematic review of safety culture dimensions and measurement tools in the petrochemical industry using the PRISMA protocol.

International journal of occupational safety and ergonomics : JOSE·2026

Same author

Unraveling the Unexpected: How Pilots Can Successfully Manage Unexpected Events.

Human factors·2026

Same author

Association Between Lower-Limb Muscle Quality, Cognitive Function and Sarcopenia in Older Adults: Cross-Sectional Study.

Bioengineering (Basel, Switzerland)·2026

Same author

Immediate Effect of Whole-Body Vibration Exercise Performed in Vertical Versus Side-Alternating Displacement Modes on Physiological Parameters, Perception of Effort, Strength and Functionality in Adults with Obesity.

Diagnostics (Basel, Switzerland)·2026

Same author

Effect of non-invasive ventilation and high-flow nasal cannula on hospital mortality in COVID-19-induced acute respiratory failure: a meta-analysis.

Einstein (Sao Paulo, Brazil)·2026

Same author

Comment on Chwalik-Pilszyk et al. Application of Polyurethane Foam as a Material for Reducing Vibration of Wheelchair User. <i>Materials</i> 2025, <i>18</i>, 1280.

Materials (Basel, Switzerland)·2025

Search research articles

Related Experiment Video

Updated: Oct 12, 2025

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

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review.

Maham Saeidi¹, Waldemar Karwowski¹, Farzad V Farahani^1,2

¹Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA.

|November 27, 2021

Summary

This review explores artificial intelligence in electroencephalography (EEG) analysis. Machine learning and deep learning models show promise for decoding brain signals in tasks like motor imagery.

Keywords:

EEG brain signals classification deep learning machine learning review

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

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

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

Published on: June 26, 2012

Related Experiment Videos

Last Updated: Oct 12, 2025

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

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

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

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

Published on: June 26, 2012

Area of Science:

Neuroscience
Computer Science
Biomedical Engineering

Background:

Electroencephalography (EEG) records brain electrical activity non-invasively.
Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is increasingly used for EEG data analysis.
Applications include pattern analysis, classification, and brain-computer interfaces.

Purpose of the Study:

To systematically review recent advances in supervised ML and DL models for EEG signal decoding and classification.
To provide a comprehensive overview of state-of-the-art EEG signal preprocessing and feature extraction techniques.
To identify effective feature extraction methods and classifier recommendations.

Main Methods:

Systematic literature search of academic databases from 2000 to present.
Focus on supervised machine learning and deep learning models for EEG analysis.
Analysis of preprocessing and feature extraction techniques.

Main Results:

ML and DL applications in mental workload and motor imagery tasks are prominent.
Convolutional neural networks (CNNs) dominate DL studies (75%).
Support vector machines (SVMs) are frequently used in ML studies (36% accuracy).
Wavelet transform is the most common feature extraction method across tasks.

Conclusions:

ML and DL significantly advance EEG signal processing and interpretation.
CNNs and SVMs are leading models for EEG classification.
Wavelet transform is a versatile feature extraction technique for EEG data.