TY - JOUR
T1 - Multi-domain feature analysis method of MI-EEG signal based on Sparse Regularity Tensor-Train decomposition
AU - Gao, Yunyuan
AU - Zhang, Congrui
AU - Fang, Feng
AU - Cammon, Jared
AU - Zhang, Yingchun
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/5
Y1 - 2023/5
N2 - Tensor analysis can comprehensively retain multidomain characteristics, which has been employed in EEG studies. However, existing EEG tensor has large dimension, making it difficult to extract features. Traditional Tucker decomposition and Canonical Polyadic decomposition(CP) decomposition algorithms have problems of low computational efficiency and weak capability to extract features. To solve the above problems, Tensor-Train(TT) decomposition is adopted to analyze the EEG tensor. Meanwhile, sparse regularization term can then be added to TT decomposition, resulting in a sparse regular TT decomposition (SR-TT). The SR-TT algorithm is proposed in this paper, which has higher accuracy and stronger generalization ability than state-of-the-art decomposition methods. The SR-TT algorithm was verified with BCI competition III and BCI competition IV dataset and achieved 86.38% and 85.36% classification accuracies, respectively. Meanwhile, compared with traditional tensor decomposition (Tucker and CP) method, the computational efficiency of the proposed algorithm was improved by 16.49 and 31.08 times in BCI competition III and 20.72 and 29.45 times more efficient in BCI competition IV. Besides, the method can leverage tensor decomposition to extract spatial features, and the analysis is performed by pairs of brain topography visualizations to show the changes of active brain regions under the task condition. In conclusion, the proposed SR-TT algorithm in the paper provides a novel insight for tensor EEG analysis.
AB - Tensor analysis can comprehensively retain multidomain characteristics, which has been employed in EEG studies. However, existing EEG tensor has large dimension, making it difficult to extract features. Traditional Tucker decomposition and Canonical Polyadic decomposition(CP) decomposition algorithms have problems of low computational efficiency and weak capability to extract features. To solve the above problems, Tensor-Train(TT) decomposition is adopted to analyze the EEG tensor. Meanwhile, sparse regularization term can then be added to TT decomposition, resulting in a sparse regular TT decomposition (SR-TT). The SR-TT algorithm is proposed in this paper, which has higher accuracy and stronger generalization ability than state-of-the-art decomposition methods. The SR-TT algorithm was verified with BCI competition III and BCI competition IV dataset and achieved 86.38% and 85.36% classification accuracies, respectively. Meanwhile, compared with traditional tensor decomposition (Tucker and CP) method, the computational efficiency of the proposed algorithm was improved by 16.49 and 31.08 times in BCI competition III and 20.72 and 29.45 times more efficient in BCI competition IV. Besides, the method can leverage tensor decomposition to extract spatial features, and the analysis is performed by pairs of brain topography visualizations to show the changes of active brain regions under the task condition. In conclusion, the proposed SR-TT algorithm in the paper provides a novel insight for tensor EEG analysis.
KW - MI-EEG
KW - Sparse regularity
KW - Tensor decomposition
KW - Tensor-Train
KW - Brain-Computer Interfaces
KW - Brain/diagnostic imaging
KW - Algorithms
KW - Electroencephalography/methods
KW - Imagination
KW - Signal Processing, Computer-Assisted
UR - http://www.scopus.com/inward/record.url?scp=85151507030&partnerID=8YFLogxK
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U2 - 10.1016/j.compbiomed.2023.106887
DO - 10.1016/j.compbiomed.2023.106887
M3 - Article
C2 - 37023540
AN - SCOPUS:85151507030
SN - 0010-4825
VL - 158
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 106887
ER -