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

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Bibliographic Details
Published in:Bioengineering (Basel) 2024-06, Vol.11 (6), p.609
Main Authors: Wu, Huanqi, Wang, Ruonan, Ma, Yuyu, Liang, Xiaoyu, Liu, Changzeng, Yu, Dexin, An, Nan, Ning, Xiaolin
Format: Article
Language:English
Subjects:
Brain mapping
Brain research
Classification
Computational linguistics
Data processing
Decoding
decoding pipeline
EEG
Electroencephalography
Functional magnetic resonance imaging
Information processing
Language processing
Machine learning
Magnetic resonance imaging
Magnetism
Magnetoencephalography
Memory
modality fusion
Multivariate analysis
MVPA
Natural language interfaces
Neuroimaging
Neurosciences
OP-MEG
Pattern analysis
Regression analysis
Resampling
Semantics
Support vector machines
Time series
Variance analysis
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Summary: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.
ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering11060609