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:	Maham Saeidi, Waldemar Karwowski, Farzad V. Farahani, Krzysztof Fiok, Redha Taiar, P. A. Hancock, Awad Al-Juaid
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	brain signals classification EEG deep learning machine learning review Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/97d37623d3624809a837fab71e23c18d
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spelling	oai:doaj.org-article:97d37623d3624809a837fab71e23c18d2021-11-25T16:59:00ZNeural Decoding of EEG Signals with Machine Learning: A Systematic Review10.3390/brainsci111115252076-3425https://doaj.org/article/97d37623d3624809a837fab71e23c18d2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3425/11/11/1525https://doaj.org/toc/2076-3425Electroencephalography (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.Maham SaeidiWaldemar KarwowskiFarzad V. FarahaniKrzysztof FiokRedha TaiarP. A. HancockAwad Al-JuaidMDPI AGarticlebrain signals classificationEEGdeep learningmachine learningreviewNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENBrain Sciences, Vol 11, Iss 1525, p 1525 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	brain signals classification EEG deep learning machine learning review Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	brain signals classification EEG deep learning machine learning review Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Maham Saeidi Waldemar Karwowski Farzad V. Farahani Krzysztof Fiok Redha Taiar P. A. Hancock Awad Al-Juaid Neural Decoding of EEG Signals with Machine Learning: A Systematic Review
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.
format	article
author	Maham Saeidi Waldemar Karwowski Farzad V. Farahani Krzysztof Fiok Redha Taiar P. A. Hancock Awad Al-Juaid
author_facet	Maham Saeidi Waldemar Karwowski Farzad V. Farahani Krzysztof Fiok Redha Taiar P. A. Hancock Awad Al-Juaid
author_sort	Maham Saeidi
title	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_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_sort	neural decoding of eeg signals with machine learning: a systematic review
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/97d37623d3624809a837fab71e23c18d
work_keys_str_mv	AT mahamsaeidi neuraldecodingofeegsignalswithmachinelearningasystematicreview AT waldemarkarwowski neuraldecodingofeegsignalswithmachinelearningasystematicreview AT farzadvfarahani neuraldecodingofeegsignalswithmachinelearningasystematicreview AT krzysztoffiok neuraldecodingofeegsignalswithmachinelearningasystematicreview AT redhataiar neuraldecodingofeegsignalswithmachinelearningasystematicreview AT pahancock neuraldecodingofeegsignalswithmachinelearningasystematicreview AT awadaljuaid neuraldecodingofeegsignalswithmachinelearningasystematicreview
_version_	1718412852681768960

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

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