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作者:

Wang, Linlin (Wang, Linlin.) | Li, Mingai (Li, Mingai.) (学者:李明爱)

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摘要:

Motor imagery (MI) decoding is the core of an intelligent rehabilitation system in brain computer interface, and it has a potential advantage by using source signals, which have higher spatial resolution and the same time resolution compared to scalp electroencephalography (EEG). However, how to delve and utilize the personalized frequency characteristic of dipoles for improving decoding performance has not been paid sufficient attention. In this paper, a novel dipole feature imaging (DFI) and a hybrid convolutional neural network (HCNN) with an embedded squeeze-and-excitation block (SEB), denoted as DFI-HCNN, are proposed for decoding MI tasks. EEG source imaging technique is used for brain source estimation, and each sub-band spectrum powers of all dipoles are calculated through frequency analysis and band division. Then, the 3D space information of dipoles is retrieved, and by using azimuthal equidistant projection algorithm it is transformed to a 2D plane, which is combined with nearest neighbor interpolation to generate multi sub-band dipole feature images. Furthermore, a HCNN is designed and applied to the ensemble of sub-band dipole feature images, from which the importance of sub-bands is acquired to adjust the corresponding attentions adaptively by SEB. Ten-fold cross-validation experiments on two public datasets achieve the comparatively higher decoding accuracies of 84.23% and 92.62%, respectively. The experiment results show that DFI is an effective feature representation, and HCNN with an embedded SEB can enhance the useful frequency information of dipoles for improving MI decoding.

关键词:

EEG source imaging Squeeze-and-excitation block Brain computer interface Hybrid convolutional neural network Motor imagery

作者机构:

  • [ 1 ] [Wang, Linlin]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 2 ] [Li, Mingai]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 3 ] [Li, Mingai]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 4 ] [Li, Mingai]Minist Educ, Engn Res Ctr Digital Community, Beijing, Peoples R China
  • [ 5 ] [Li, Mingai]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 6 ] [Li, Mingai]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 7 ] [Li, Mingai]Minist Educ, Engn Res Ctr Digital Community, Beijing 100124, Peoples R China

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来源 :

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING

ISSN: 0208-5216

年份: 2023

期: 4

卷: 43

页码: 751-762

6 . 4 0 0

JCR@2022

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