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Decoding N400m Evoked Component: A Tutorial on Multivariate Pattern Analysis for OP-MEG Data.

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Název: Decoding N400m Evoked Component: A Tutorial on Multivariate Pattern Analysis for OP-MEG Data.
Autoři: Wu, Huanqi, Wang, Ruonan, Ma, Yuyu, Liang, Xiaoyu, Liu, Changzeng, Yu, Dexin, An, Nan, Ning, Xiaolin
Zdroj: Bioengineering (Basel); Jun2024, Vol. 11 Issue 6, p609, 18p
Témata: FUNCTIONAL magnetic resonance imaging, MULTIVARIATE analysis, COGNITIVE computing, DATA analysis, RESAMPLING (Statistics), COGNITIVE neuroscience
Abstrakt: Multivariate pattern analysis (MVPA) has played an extensive role in interpreting brain activity, which has been applied in studies with modalities such as functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Electroencephalography (EEG). The advent of wearable MEG systems based on optically pumped magnetometers (OPMs), i.e., OP-MEG, has broadened the application of bio-magnetism in the realm of neuroscience. Nonetheless, it also raises challenges in temporal decoding analysis due to the unique attributes of OP-MEG itself. The efficacy of decoding performance utilizing multimodal fusion, such as MEG-EEG, also remains to be elucidated. In this regard, we investigated the impact of several factors, such as processing methods, models and modalities, on the decoding outcomes of OP-MEG. Our findings indicate that the number of averaged trials, dimensionality reduction (DR) methods, and the number of cross-validation folds significantly affect the decoding performance of OP-MEG data. Additionally, decoding results vary across modalities and fusion strategy. In contrast, decoder type, resampling frequency, and sliding window length exert marginal effects. Furthermore, we introduced mutual information (MI) to investigate how information loss due to OP-MEG data processing affect decoding accuracy. Our study offers insights for linear decoding research using OP-MEG and expand its application in the fields of cognitive neuroscience. [ABSTRACT FROM AUTHOR]
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  Data: Decoding N400m Evoked Component: A Tutorial on Multivariate Pattern Analysis for OP-MEG Data.
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  Data: <searchLink fieldCode="AR" term="%22Wu%2C+Huanqi%22">Wu, Huanqi</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Ruonan%22">Wang, Ruonan</searchLink><br /><searchLink fieldCode="AR" term="%22Ma%2C+Yuyu%22">Ma, Yuyu</searchLink><br /><searchLink fieldCode="AR" term="%22Liang%2C+Xiaoyu%22">Liang, Xiaoyu</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Changzeng%22">Liu, Changzeng</searchLink><br /><searchLink fieldCode="AR" term="%22Yu%2C+Dexin%22">Yu, Dexin</searchLink><br /><searchLink fieldCode="AR" term="%22An%2C+Nan%22">An, Nan</searchLink><br /><searchLink fieldCode="AR" term="%22Ning%2C+Xiaolin%22">Ning, Xiaolin</searchLink>
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  Data: Bioengineering (Basel); Jun2024, Vol. 11 Issue 6, p609, 18p
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  Data: <searchLink fieldCode="DE" term="%22FUNCTIONAL+magnetic+resonance+imaging%22">FUNCTIONAL magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22MULTIVARIATE+analysis%22">MULTIVARIATE analysis</searchLink><br /><searchLink fieldCode="DE" term="%22COGNITIVE+computing%22">COGNITIVE computing</searchLink><br /><searchLink fieldCode="DE" term="%22DATA+analysis%22">DATA analysis</searchLink><br /><searchLink fieldCode="DE" term="%22RESAMPLING+%28Statistics%29%22">RESAMPLING (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22COGNITIVE+neuroscience%22">COGNITIVE neuroscience</searchLink>
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  Label: Abstract
  Group: Ab
  Data: Multivariate pattern analysis (MVPA) has played an extensive role in interpreting brain activity, which has been applied in studies with modalities such as functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Electroencephalography (EEG). The advent of wearable MEG systems based on optically pumped magnetometers (OPMs), i.e., OP-MEG, has broadened the application of bio-magnetism in the realm of neuroscience. Nonetheless, it also raises challenges in temporal decoding analysis due to the unique attributes of OP-MEG itself. The efficacy of decoding performance utilizing multimodal fusion, such as MEG-EEG, also remains to be elucidated. In this regard, we investigated the impact of several factors, such as processing methods, models and modalities, on the decoding outcomes of OP-MEG. Our findings indicate that the number of averaged trials, dimensionality reduction (DR) methods, and the number of cross-validation folds significantly affect the decoding performance of OP-MEG data. Additionally, decoding results vary across modalities and fusion strategy. In contrast, decoder type, resampling frequency, and sliding window length exert marginal effects. Furthermore, we introduced mutual information (MI) to investigate how information loss due to OP-MEG data processing affect decoding accuracy. Our study offers insights for linear decoding research using OP-MEG and expand its application in the fields of cognitive neuroscience. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Bioengineering (Basel) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2024
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