Rapidly Decoding Image Categories From MEG Data Using a Multivariate Short-Time FC Pattern Analysis Approach

Liu, Chunyu; Kang, Yi; Zhang, Lingxi; Zhang, Jiacai

doi:10.1109/jbhi.2020.3008731

Cited by 6 publications

(2 citation statements)

References 63 publications

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“…Finally, the tool stimuli included kitchen utensils, farm implements, and other common indoor tools, comprising 40 items, each with four exemplars. 1) The results of decoding the MEG data: For constructing a brain network pattern in the MEG data, we adopt the results of the references [26] to construct the optimal brain network pattern under four visual stimuli. The decoding results were the average of all subjects, and each subjects' results were obtained from the average of the ten-fold cross-validation.…”

Section: B Meg Datamentioning

confidence: 99%

s-TBN: A New Neural Decoding Model to Identify Stimulus Categories From Brain Activity Patterns

Liu,

Cao,

Zhang

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Neural decoding is still a challenging and a hot 1 topic in neurocomputing science. Recently, many studies have 2 shown that brain network patterns containing rich spatiotem-3 poral structural information represent the brain's activation 4 information under external stimuli. In the traditional method, 5 brain network features are directly obtained using the standard 6 machine learning method and provide to a classifier, subsequently 7 decoding external stimuli. However, this method cannot effec-8 tively extract the multidimensional structural information hidden 9 in the brain network. Furthermore, studies on tensors have 10 show that the tensor decomposition model can fully mine unique 11 spatiotemporal structural characteristics of a spatiotemporal 12 structure in data with a multidimensional structure. This re-13 search proposed a stimulus-constrained Tensor Brain Network (s-14 TBN) model that involves the tensor decomposition and stimulus 15 category-constraint information. The model was verified on real 16 neuroimaging data obtained via magnetoencephalograph and 17 functional mangetic resonance imaging). Experimental results 18 show that the s-TBN model achieve accuracy matrices of greater 19 than 11.06% and 18.46% on the accuracy matrix compared with 20 other methods on two modal datasets. These results prove the 21 superiority of extracting discriminative characteristics using the 22 STN model, especially for decoding object stimuli with semantic 23 information. 24 Index Terms-Neural decoding, Brain network, Tensor decom-25 position, Tensor brain network 26 I. INTRODUCTION 27 N EURAL decoding is achieved by analyzing the neural 28 signals pattern collected using the noninvasive device 29 to evaluate the brain's activation patterns in response to 30 specific visual stimuli. Subsequently, these patterns are used to 31 deduce the external stimulus categories inversely [1]. One vital 32 step in this process is establishing the mapping relationships 33 among the neural activation patterns, neural signal patterns, 34 and external stimuli. Previous research focused on the brain 35 individual-area model to establish this mapping relationship 36 (such as a single brain area or channel signals) [2]. However, 37 the study of this mapping from the network model on the level 38 of the entire brain or the local system has become a research 39 hotspot [3]. 40 Brain network structure data typically involve various di-41 mensions, such as time and space, and indirectly represent 42

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Section: B Meg Datamentioning

confidence: 99%

s-TBN: A New Neural Decoding Model to Identify Stimulus Categories From Brain Activity Patterns

Liu,

Cao,

Zhang

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…1) The results of decoding MEG data: For the method of constructing brain network pattern in MEG data, we adopt the results of the references [30] to construct the optimal brain network pattern under four visual stimulus. The decoding results were the average of all subjects, and the results of each subject were obtained through the average of the tenfold cross-validation.…”

Section: A the Effect Of Modelmentioning

confidence: 99%

STN: a new tensor network method to identify stimulus category from brain activity pattern

Liu¹,

Zhang²

2022

Preprint

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Neural decoding is still a challenge and hot topic in neurocomputing science. Recently, many studies have shown that brain network pattern containing rich spatial and temporal structure information, which represented the activation information of brain under external stimuli. The traditional method is to extract brain network features directly from the common machine learning method, then put these features into the classifier, and realize to decode external stimuli. However, this method cannot effectively extract the multi-dimensional structural information, which is hidden in the brain network. The tensor researchers show that the tensor decomposition model can fully mine unique spatio-temporal structure characteristics in multi-dimensional structure data. This research proposed a stimulus constrain tensor brain model(STN), which involved the tensor decomposition idea and stimulus category constraint information. The model was verified on the real neuroimaging data sets (MEG and fMRI). The experimental results show that the STN model achieved more 11.06% and 18.46% compared with others methods on two modal data sets. These results imply the superiority of extracting discriminative characteristics about STN model, especially for decoding object stimuli with semantic information.

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Categorizing objects from MEG signals using EEGNet

et al. 2021

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Rapidly Decoding Image Categories From MEG Data Using a Multivariate Short-Time FC Pattern Analysis Approach

Cited by 6 publications

References 63 publications

s-TBN: A New Neural Decoding Model to Identify Stimulus Categories From Brain Activity Patterns

s-TBN: A New Neural Decoding Model to Identify Stimulus Categories From Brain Activity Patterns

STN: a new tensor network method to identify stimulus category from brain activity pattern

Categorizing objects from MEG signals using EEGNet

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