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The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance

Numerous brain–machine interface (BMI) studies have shown that various frequency bands (alpha, beta, and gamma bands) can be utilized in BMI experiments and modulated as neural information for machine control after several BMI learning trial sessions. In addition to frequency range as a neural featu...

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Autores principales: Jung, Wongyu, Lim, Seokbeen, Kwak, Youngjong, Sim, Jeongeun, Park, Jinsick, Jang, Dongpyo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8541475/
https://www.ncbi.nlm.nih.gov/pubmed/34695942
http://dx.doi.org/10.3390/s21206729
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author Jung, Wongyu
Lim, Seokbeen
Kwak, Youngjong
Sim, Jeongeun
Park, Jinsick
Jang, Dongpyo
author_facet Jung, Wongyu
Lim, Seokbeen
Kwak, Youngjong
Sim, Jeongeun
Park, Jinsick
Jang, Dongpyo
author_sort Jung, Wongyu
collection PubMed
description Numerous brain–machine interface (BMI) studies have shown that various frequency bands (alpha, beta, and gamma bands) can be utilized in BMI experiments and modulated as neural information for machine control after several BMI learning trial sessions. In addition to frequency range as a neural feature, various areas of the brain, such as the motor cortex or parietal cortex, have been selected as BMI target brain regions. However, although the selection of target frequency and brain region appears to be crucial in obtaining optimal BMI performance, the direct comparison of BMI learning performance as it relates to various brain regions and frequency bands has not been examined in detail. In this study, ECoG-based BMI learning performances were compared using alpha, beta, and gamma bands, respectively, in a single rodent model. Brain area dependence of learning performance was also evaluated in the frontal cortex, the motor cortex, and the parietal cortex. The findings indicated that BMI learning performance was best in the case of the gamma frequency band and worst in the alpha band (one-way ANOVA, F = 4.41, p < 0.05). In brain area dependence experiments, better BMI learning performance appears to be shown in the primary motor cortex (one-way ANOVA, F = 4.36, p < 0.05). In the frontal cortex, two out of four animals failed to learn the feeding tube control even after a maximum of 10 sessions. In conclusion, the findings reported in this study suggest that the selection of target frequency and brain region should be carefully considered when planning BMI protocols and for performing optimized BMI.
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spelling pubmed-85414752021-10-24 The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance Jung, Wongyu Lim, Seokbeen Kwak, Youngjong Sim, Jeongeun Park, Jinsick Jang, Dongpyo Sensors (Basel) Article Numerous brain–machine interface (BMI) studies have shown that various frequency bands (alpha, beta, and gamma bands) can be utilized in BMI experiments and modulated as neural information for machine control after several BMI learning trial sessions. In addition to frequency range as a neural feature, various areas of the brain, such as the motor cortex or parietal cortex, have been selected as BMI target brain regions. However, although the selection of target frequency and brain region appears to be crucial in obtaining optimal BMI performance, the direct comparison of BMI learning performance as it relates to various brain regions and frequency bands has not been examined in detail. In this study, ECoG-based BMI learning performances were compared using alpha, beta, and gamma bands, respectively, in a single rodent model. Brain area dependence of learning performance was also evaluated in the frontal cortex, the motor cortex, and the parietal cortex. The findings indicated that BMI learning performance was best in the case of the gamma frequency band and worst in the alpha band (one-way ANOVA, F = 4.41, p < 0.05). In brain area dependence experiments, better BMI learning performance appears to be shown in the primary motor cortex (one-way ANOVA, F = 4.36, p < 0.05). In the frontal cortex, two out of four animals failed to learn the feeding tube control even after a maximum of 10 sessions. In conclusion, the findings reported in this study suggest that the selection of target frequency and brain region should be carefully considered when planning BMI protocols and for performing optimized BMI. MDPI 2021-10-11 /pmc/articles/PMC8541475/ /pubmed/34695942 http://dx.doi.org/10.3390/s21206729 Text en © 2021 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
Jung, Wongyu
Lim, Seokbeen
Kwak, Youngjong
Sim, Jeongeun
Park, Jinsick
Jang, Dongpyo
The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance
title The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance
title_full The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance
title_fullStr The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance
title_full_unstemmed The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance
title_short The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance
title_sort influence of frequency bands and brain region on ecog-based bmi learning performance
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8541475/
https://www.ncbi.nlm.nih.gov/pubmed/34695942
http://dx.doi.org/10.3390/s21206729
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