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Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography(✩)

Previous studies have attempted to separate single trial neural responses for events a person is likely to remember from those they are likely to forget using machine learning classification methods. Successful single tria classification holds potential for translation into the clinical realm for re...

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Autores principales: Mirjalili, Soroush, Powell, Patrick, Strunk, Jonathan, James, Taylor, Duarte, Audrey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8824531/
https://www.ncbi.nlm.nih.gov/pubmed/34954026
http://dx.doi.org/10.1016/j.neuroimage.2021.118851
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author Mirjalili, Soroush
Powell, Patrick
Strunk, Jonathan
James, Taylor
Duarte, Audrey
author_facet Mirjalili, Soroush
Powell, Patrick
Strunk, Jonathan
James, Taylor
Duarte, Audrey
author_sort Mirjalili, Soroush
collection PubMed
description Previous studies have attempted to separate single trial neural responses for events a person is likely to remember from those they are likely to forget using machine learning classification methods. Successful single tria classification holds potential for translation into the clinical realm for real-time detection of memory and other cognitive states to provide real-time interventions (i.e., brain-computer interfaces). However, most of these studies—and classification analyses in general—do not make clear if the chosen methodology is optimally suited for the classification of memory-related brain states. To address this problem, we systematically compared different methods for every step of classification (i.e., feature extraction, feature selection, classifier selection) to investigate which methods work best for decoding episodic memory brain states—the first analysis of its kind. Using an adult lifespan sample EEG dataset collected during performance of an episodic context encoding and retrieva task, we found that no specific feature type (including Common Spatial Pattern (CSP)-based features, mean, variance, correlation, features based on AR model, entropy, phase, and phase synchronization) outperformed others consistently in distinguishing different memory classes. However, extracting all of these feature types consistently outperformed extracting only one type of feature. Additionally, the combination of filtering and sequential forward selection was the optimal method to select the effective features compared to filtering alone or performing no feature selection at all. Moreover, although all classifiers performed at a fairly similar level, LASSO was con sistently the highest performing classifier compared to other commonly used options (i.e., naïve Bayes, SVM, and logistic regression) while naïve Bayes was the fastest classifier. Lastly, for multiclass classification (i.e., levels of context memory confidence and context feature perception), generalizing the binary classification using the binary decision tree performed better than the voting or one versus rest method. These methods were shown to outperform alternative approaches for three orthogonal datasets (i.e., EEG working memory, EEG motor imagery, and MEG working memory), supporting their generalizability. Our results provide an optimized methodological process for classifying single-trial neural data and provide important insight and recommendations for a cognitive neuroscientist’s ability to make informed choices at all stages of the classification process for predicting memory and other cognitive states.
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spelling pubmed-88245312022-02-15 Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography(✩) Mirjalili, Soroush Powell, Patrick Strunk, Jonathan James, Taylor Duarte, Audrey Neuroimage Article Previous studies have attempted to separate single trial neural responses for events a person is likely to remember from those they are likely to forget using machine learning classification methods. Successful single tria classification holds potential for translation into the clinical realm for real-time detection of memory and other cognitive states to provide real-time interventions (i.e., brain-computer interfaces). However, most of these studies—and classification analyses in general—do not make clear if the chosen methodology is optimally suited for the classification of memory-related brain states. To address this problem, we systematically compared different methods for every step of classification (i.e., feature extraction, feature selection, classifier selection) to investigate which methods work best for decoding episodic memory brain states—the first analysis of its kind. Using an adult lifespan sample EEG dataset collected during performance of an episodic context encoding and retrieva task, we found that no specific feature type (including Common Spatial Pattern (CSP)-based features, mean, variance, correlation, features based on AR model, entropy, phase, and phase synchronization) outperformed others consistently in distinguishing different memory classes. However, extracting all of these feature types consistently outperformed extracting only one type of feature. Additionally, the combination of filtering and sequential forward selection was the optimal method to select the effective features compared to filtering alone or performing no feature selection at all. Moreover, although all classifiers performed at a fairly similar level, LASSO was con sistently the highest performing classifier compared to other commonly used options (i.e., naïve Bayes, SVM, and logistic regression) while naïve Bayes was the fastest classifier. Lastly, for multiclass classification (i.e., levels of context memory confidence and context feature perception), generalizing the binary classification using the binary decision tree performed better than the voting or one versus rest method. These methods were shown to outperform alternative approaches for three orthogonal datasets (i.e., EEG working memory, EEG motor imagery, and MEG working memory), supporting their generalizability. Our results provide an optimized methodological process for classifying single-trial neural data and provide important insight and recommendations for a cognitive neuroscientist’s ability to make informed choices at all stages of the classification process for predicting memory and other cognitive states. 2022-02-15 2021-12-22 /pmc/articles/PMC8824531/ /pubmed/34954026 http://dx.doi.org/10.1016/j.neuroimage.2021.118851 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) )
spellingShingle Article
Mirjalili, Soroush
Powell, Patrick
Strunk, Jonathan
James, Taylor
Duarte, Audrey
Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography(✩)
title Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography(✩)
title_full Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography(✩)
title_fullStr Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography(✩)
title_full_unstemmed Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography(✩)
title_short Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography(✩)
title_sort evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography(✩)
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8824531/
https://www.ncbi.nlm.nih.gov/pubmed/34954026
http://dx.doi.org/10.1016/j.neuroimage.2021.118851
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