2021
DOI: 10.3390/brainsci11111525
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Abstract: 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, thi… Show more

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Cited by 71 publications
(23 citation statements)
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References 278 publications
(522 reference statements)
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“…The reclassification results obtained by means of the linear discriminant analysis (Table 1 A and B) confirm this visual assessment. Apart from linear discriminant analysis, other methods, e.g., neuronal networks, are available for classification tasks [ 33 ]. However, such alternative methods would also be limited by the information content of the available variables.…”
Section: Discussionmentioning
confidence: 99%
“…The reclassification results obtained by means of the linear discriminant analysis (Table 1 A and B) confirm this visual assessment. Apart from linear discriminant analysis, other methods, e.g., neuronal networks, are available for classification tasks [ 33 ]. However, such alternative methods would also be limited by the information content of the available variables.…”
Section: Discussionmentioning
confidence: 99%
“…Several surveys and studies have been conducted, and the automatic learning methods (i.e., machine learning (ML)) have proven their effectiveness in recognizing EEG wave patterns [23][24][25][26][27][28][29][30]. A key advantage of ML is manipulating multimodal objectively; and modeling hidden relationships in complex datasets with heterogeneous distribution using advanced mathematical techniques [31,32]. The learning strategy is particularly based on supervised learning (i.e., the algorithms learn from labeled training data to create a model that can generate predictions based on unknown data) and unsupervised learning (i.e., the algorithms analyze and cluster unlabeled data, and discover hidden patterns of data clusters without the need of human intervention).…”
Section: Introductionmentioning
confidence: 99%
“…In the literature, independent component analysis (ICA) has been investigated as a chosen technique for artifact rejection to improve the quality of EEG signals. The ICA has been widely used in EEG signal analysis and brain-computer interface (BCI) [31][32][33]. Khoshnevis and Sankar [34] confirmed that the blind source separation in ICA allows estimation of independent components (ICs) from multiple mixed observations without prior knowledge about brain activity to remove correlation between the channels [35].…”
Section: Introductionmentioning
confidence: 99%
“…One approach to solve this problem uses alternative neuroimaging devices, such as electroencephalography (EEG), that require weaker constraint and lower costs [21,22]. However, signals collected with these devices are noisier than fMRI, thus making it difficult to recover rich mental information [3,10].…”
Section: Introductionmentioning
confidence: 99%