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A Method for the Study of Cerebellar Cognitive Function—Re-Cognition and Validation of Error-Related Potentials

The cerebellar region has four times as many brain cells as the brain, but whether the cerebellum functions in cognition, and how it does so, remain unexplored. In order to verify whether the cerebellum is involved in cognition, we chose to investigate whether the cerebellum is involved in the proce...

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Autores principales: Mu, Bo, Niu, Chang, Shi, Jingping, Li, Rumei, Yu, Chao, Yin, Kuiying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9497277/
https://www.ncbi.nlm.nih.gov/pubmed/36138909
http://dx.doi.org/10.3390/brainsci12091173
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author Mu, Bo
Niu, Chang
Shi, Jingping
Li, Rumei
Yu, Chao
Yin, Kuiying
author_facet Mu, Bo
Niu, Chang
Shi, Jingping
Li, Rumei
Yu, Chao
Yin, Kuiying
author_sort Mu, Bo
collection PubMed
description The cerebellar region has four times as many brain cells as the brain, but whether the cerebellum functions in cognition, and how it does so, remain unexplored. In order to verify whether the cerebellum is involved in cognition, we chose to investigate whether the cerebellum is involved in the process of error judgment. We designed an experiment in which we could activate the subject’s error-related potentials (ErrP). We recruited 26 subjects and asked them to wear EEG caps with cerebellar regions designed by us to participate in the experiment so that we could record their EEG activity throughout the experiment. We successfully mitigated the majority of noise interference after a series of pre-processing of the data collected from each subject. Our analysis of the preprocessed data revealed that our experiment successfully activated ErrP, and that the EEG signals, including the cerebellum, were significantly different when subjects made errors compared to when they made correct judgments. We designed a feature extraction method that requires selecting channels with large differences under different classifications, firstly by extracting the time-frequency features of these channels, and then screening these features with sequence backward feature (SBS) selection. We use the extracted features as the input and different event types in EEG data as the labels for multiple classifiers to classify the data in the executive and feedback segments, where the average accuracy for two-class classification of executive segments can reach 80.5%. The major contribution of our study is the discovery of the presence of ErrP in cerebellar regions and the extraction of an effective feature extraction method for EEG data.
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spelling pubmed-94972772022-09-23 A Method for the Study of Cerebellar Cognitive Function—Re-Cognition and Validation of Error-Related Potentials Mu, Bo Niu, Chang Shi, Jingping Li, Rumei Yu, Chao Yin, Kuiying Brain Sci Article The cerebellar region has four times as many brain cells as the brain, but whether the cerebellum functions in cognition, and how it does so, remain unexplored. In order to verify whether the cerebellum is involved in cognition, we chose to investigate whether the cerebellum is involved in the process of error judgment. We designed an experiment in which we could activate the subject’s error-related potentials (ErrP). We recruited 26 subjects and asked them to wear EEG caps with cerebellar regions designed by us to participate in the experiment so that we could record their EEG activity throughout the experiment. We successfully mitigated the majority of noise interference after a series of pre-processing of the data collected from each subject. Our analysis of the preprocessed data revealed that our experiment successfully activated ErrP, and that the EEG signals, including the cerebellum, were significantly different when subjects made errors compared to when they made correct judgments. We designed a feature extraction method that requires selecting channels with large differences under different classifications, firstly by extracting the time-frequency features of these channels, and then screening these features with sequence backward feature (SBS) selection. We use the extracted features as the input and different event types in EEG data as the labels for multiple classifiers to classify the data in the executive and feedback segments, where the average accuracy for two-class classification of executive segments can reach 80.5%. The major contribution of our study is the discovery of the presence of ErrP in cerebellar regions and the extraction of an effective feature extraction method for EEG data. MDPI 2022-09-01 /pmc/articles/PMC9497277/ /pubmed/36138909 http://dx.doi.org/10.3390/brainsci12091173 Text en © 2022 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 Article
Mu, Bo
Niu, Chang
Shi, Jingping
Li, Rumei
Yu, Chao
Yin, Kuiying
A Method for the Study of Cerebellar Cognitive Function—Re-Cognition and Validation of Error-Related Potentials
title A Method for the Study of Cerebellar Cognitive Function—Re-Cognition and Validation of Error-Related Potentials
title_full A Method for the Study of Cerebellar Cognitive Function—Re-Cognition and Validation of Error-Related Potentials
title_fullStr A Method for the Study of Cerebellar Cognitive Function—Re-Cognition and Validation of Error-Related Potentials
title_full_unstemmed A Method for the Study of Cerebellar Cognitive Function—Re-Cognition and Validation of Error-Related Potentials
title_short A Method for the Study of Cerebellar Cognitive Function—Re-Cognition and Validation of Error-Related Potentials
title_sort method for the study of cerebellar cognitive function—re-cognition and validation of error-related potentials
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9497277/
https://www.ncbi.nlm.nih.gov/pubmed/36138909
http://dx.doi.org/10.3390/brainsci12091173
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