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

Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, g...

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Autores principales: Saeidi, Maham, Karwowski, Waldemar, Farahani, Farzad V., Fiok, Krzysztof, Taiar, Redha, Hancock, P. A., Al-Juaid, Awad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615531/
https://www.ncbi.nlm.nih.gov/pubmed/34827524
http://dx.doi.org/10.3390/brainsci11111525
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author Saeidi, Maham
Karwowski, Waldemar
Farahani, Farzad V.
Fiok, Krzysztof
Taiar, Redha
Hancock, P. A.
Al-Juaid, Awad
author_facet Saeidi, Maham
Karwowski, Waldemar
Farahani, Farzad V.
Fiok, Krzysztof
Taiar, Redha
Hancock, P. A.
Al-Juaid, Awad
author_sort Saeidi, Maham
collection PubMed
description Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.
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spelling pubmed-86155312021-11-26 Neural Decoding of EEG Signals with Machine Learning: A Systematic Review Saeidi, Maham Karwowski, Waldemar Farahani, Farzad V. Fiok, Krzysztof Taiar, Redha Hancock, P. A. Al-Juaid, Awad Brain Sci Systematic Review Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review. MDPI 2021-11-18 /pmc/articles/PMC8615531/ /pubmed/34827524 http://dx.doi.org/10.3390/brainsci11111525 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Systematic Review
Saeidi, Maham
Karwowski, Waldemar
Farahani, Farzad V.
Fiok, Krzysztof
Taiar, Redha
Hancock, P. A.
Al-Juaid, Awad
Neural Decoding of EEG Signals with Machine Learning: A Systematic Review
title Neural Decoding of EEG Signals with Machine Learning: A Systematic Review
title_full Neural Decoding of EEG Signals with Machine Learning: A Systematic Review
title_fullStr Neural Decoding of EEG Signals with Machine Learning: A Systematic Review
title_full_unstemmed Neural Decoding of EEG Signals with Machine Learning: A Systematic Review
title_short Neural Decoding of EEG Signals with Machine Learning: A Systematic Review
title_sort neural decoding of eeg signals with machine learning: a systematic review
topic Systematic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615531/
https://www.ncbi.nlm.nih.gov/pubmed/34827524
http://dx.doi.org/10.3390/brainsci11111525
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