Motor imagery EEG task recognition using a nonlinear Granger causality feature extraction and an improved Salp swarm feature selection
•Brain network features were extracted by a Granger causal analysis.•Brain functional connectivity contributes to improving MI task classification.•An effective swarm optimization algorithm are used for feature selection. In the study of motor imagery (MI) brain-computer interfaces (BCIs), how to im...
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| Published in: | Biomedical signal processing and control Vol. 88; p. 105626 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.02.2024
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| ISSN: | 1746-8094, 1746-8108 |
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| Abstract | •Brain network features were extracted by a Granger causal analysis.•Brain functional connectivity contributes to improving MI task classification.•An effective swarm optimization algorithm are used for feature selection.
In the study of motor imagery (MI) brain-computer interfaces (BCIs), how to improve task classification accuracy has been always one of major challenges in the applications of MI-BCIs. As a type of crucial temporal and spatial feature, nonlinear Granger Causality (NGC) analysis was applied to feature extraction of MI-electroencephalogram (EEG) signals because the constructed brain network features can reflect the causal relationship between different channels in various brain regions. However, the MI-BCI task recognition often suffer from the information redundancy of NGC features, and these redundant features will increase the complexity of the machine learning models and accordingly reduce the prediction accuracy of the classification algorithms. To address this problem, this paper proposes a step-by-step tent chaos simulated annealing salp swarm feature selection (STCSA_SaSFS) algorithm to select an optimal set of features in a wrapper feature selection model. Then, the effectiveness of this feature selection method is verified using a support vector machine (SVM) classifier. Through the study of task related MI-BCI EEG data from ten subjects, the experiments showed that the highest classification accuracy of NGC feature extraction plus STCSA_SaSFS reached 97.19%, and the average classification accuracy was 89.57%. This average classification accuracy was 20.07% higher than that of NGC feature extraction without any feature selection, and it is also 2.96% higher than that of NGC feature extraction plus a traditional SaSFS algorithm. The effectiveness of STCSA_SaSFS was also compared with that of other smart swarm optimization algorithms, such as the sparrow search feature selection algorithm (SpSFS). STCSA_SaSFS outperforms SpSFS with an average classification accuracy of 8.07%. The algorithm was validated using a public dataset validation consisting of 10 subjects, which ultimately showed that the feature selection method proposed in this paper (STSA_SaSAFS) has a large advantage in the classification performance of motor imagery brain-computer interface tasks. |
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| AbstractList | •Brain network features were extracted by a Granger causal analysis.•Brain functional connectivity contributes to improving MI task classification.•An effective swarm optimization algorithm are used for feature selection.
In the study of motor imagery (MI) brain-computer interfaces (BCIs), how to improve task classification accuracy has been always one of major challenges in the applications of MI-BCIs. As a type of crucial temporal and spatial feature, nonlinear Granger Causality (NGC) analysis was applied to feature extraction of MI-electroencephalogram (EEG) signals because the constructed brain network features can reflect the causal relationship between different channels in various brain regions. However, the MI-BCI task recognition often suffer from the information redundancy of NGC features, and these redundant features will increase the complexity of the machine learning models and accordingly reduce the prediction accuracy of the classification algorithms. To address this problem, this paper proposes a step-by-step tent chaos simulated annealing salp swarm feature selection (STCSA_SaSFS) algorithm to select an optimal set of features in a wrapper feature selection model. Then, the effectiveness of this feature selection method is verified using a support vector machine (SVM) classifier. Through the study of task related MI-BCI EEG data from ten subjects, the experiments showed that the highest classification accuracy of NGC feature extraction plus STCSA_SaSFS reached 97.19%, and the average classification accuracy was 89.57%. This average classification accuracy was 20.07% higher than that of NGC feature extraction without any feature selection, and it is also 2.96% higher than that of NGC feature extraction plus a traditional SaSFS algorithm. The effectiveness of STCSA_SaSFS was also compared with that of other smart swarm optimization algorithms, such as the sparrow search feature selection algorithm (SpSFS). STCSA_SaSFS outperforms SpSFS with an average classification accuracy of 8.07%. The algorithm was validated using a public dataset validation consisting of 10 subjects, which ultimately showed that the feature selection method proposed in this paper (STSA_SaSAFS) has a large advantage in the classification performance of motor imagery brain-computer interface tasks. |
| ArticleNumber | 105626 |
| Author | Ma, Shuang Chen, Xiaoyan Ma, Pengfei Liu, Huanzi Lin, Ruijing Dong, Chaoyi Zhou, Peng |
| Author_xml | – sequence: 1 givenname: Ruijing orcidid: 0000-0002-7271-5430 surname: Lin fullname: Lin, Ruijing organization: College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China – sequence: 2 givenname: Chaoyi orcidid: 0000-0001-8433-8903 surname: Dong fullname: Dong, Chaoyi email: dongchaoyi@imut.edu.cn organization: College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China – sequence: 3 givenname: Peng surname: Zhou fullname: Zhou, Peng organization: College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China – sequence: 4 givenname: Pengfei orcidid: 0000-0003-1497-5100 surname: Ma fullname: Ma, Pengfei organization: College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China – sequence: 5 givenname: Shuang surname: Ma fullname: Ma, Shuang organization: College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China – sequence: 6 givenname: Xiaoyan orcidid: 0000-0002-6127-959X surname: Chen fullname: Chen, Xiaoyan organization: College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China – sequence: 7 givenname: Huanzi orcidid: 0000-0002-0846-0664 surname: Liu fullname: Liu, Huanzi organization: College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China |
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| Keywords | Feature selection Motor imagery Granger Causality analysis Salp Swarm algorithm Smart swarm optimization |
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