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Systematic Review

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

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Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
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Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21218, USA
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MATIM, Moulin de la Housse, Université de Reims Champagne Ardenne, CEDEX 02, 51687 Reims, France
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Department of Psychology, University of Central Florida, Orlando, FL 32816, USA
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Industrial Engineering Department, Taif University, Taif 26571, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Academic Editor: Natsue Yoshimura
Brain Sci. 2021, 11(11), 1525; https://doi.org/10.3390/brainsci11111525
Received: 1 October 2021 / Revised: 4 November 2021 / Accepted: 11 November 2021 / Published: 18 November 2021
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. View Full-Text
Keywords: brain signals classification; EEG; deep learning; machine learning; review brain signals classification; EEG; deep learning; machine learning; review
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MDPI and ACS Style

Saeidi, M.; Karwowski, W.; Farahani, F.V.; Fiok, K.; Taiar, R.; Hancock, P.A.; Al-Juaid, A. Neural Decoding of EEG Signals with Machine Learning: A Systematic Review. Brain Sci. 2021, 11, 1525. https://doi.org/10.3390/brainsci11111525

AMA Style

Saeidi M, Karwowski W, Farahani FV, Fiok K, Taiar R, Hancock PA, Al-Juaid A. Neural Decoding of EEG Signals with Machine Learning: A Systematic Review. Brain Sciences. 2021; 11(11):1525. https://doi.org/10.3390/brainsci11111525

Chicago/Turabian Style

Saeidi, Maham, Waldemar Karwowski, Farzad V. Farahani, Krzysztof Fiok, Redha Taiar, P. A. Hancock, and Awad Al-Juaid. 2021. "Neural Decoding of EEG Signals with Machine Learning: A Systematic Review" Brain Sciences 11, no. 11: 1525. https://doi.org/10.3390/brainsci11111525

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