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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| Vydáno v: | Biomedical signal processing and control Ročník 88; s. 105626 |
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| Jazyk: | angličtina |
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Elsevier Ltd
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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| Cites_doi | 10.2307/1912791 10.1142/S0129065713500135 10.1109/IJCNN.2016.7727644 10.1109/ACCESS.2020.3018962 10.1016/j.bspc.2016.10.015 10.1016/S1388-2457(02)00057-3 10.1109/TBME.2004.827072 10.1016/j.compbiomed.2019.103495 10.1103/PhysRevE.73.066216 10.1016/0013-4694(92)90133-3 10.1016/j.sigpro.2008.01.026 10.1046/j.1365-294X.2002.01650.x 10.1016/j.advengsoft.2017.07.002 10.1007/s00521-016-2236-5 10.1155/2019/7895924 10.1155/2016/1489692 |
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| Keywords | Feature selection Motor imagery Granger Causality analysis Salp Swarm algorithm Smart swarm optimization |
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| References | Setiono, Liu (b0090) 1997; 8 Dupanloup, Schneider, Excoffier (b0160) 2002; 11 Wolpaw, Birbaumer, Mcfarland (b0010) 2002; 113 Pengfei, Chaoyi, Ruijing (b0045) 2022; 371 Aiming, Kun, Quan (b0110) 2017; 17 Udhaya Kumar, Hannah (b0115) 2017; 28 Granger (b0125) 1969 Heming, Jinduo, Wenlong (b0170) 2019; 7 Chakladar, Dey, Roy (b0100) 2020; 60 Ocak (b0095) 2008; 88 Brunato M, Battiti R. X-mifs: Exact mutual information for feature selection[C]. //2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016: 3469-3476. Vikas, Subrata, Dipti (b0085) 2004; 15 Lai, Ibrahim, Abdullah (b0035) 2019, 2019. Minmin, Aimin, Feixiang (b0120) 2018; 30 Xinxin (b0135) 2015; 28 Junhua, Jianyi, Qibin (b0030) 2013; 23 Trad, Al-Ani, Jemni (b0055) 2016; 7 Zhao, He (b0040) 2013 Shuang, Chaoyi, Tingting (b0060) 2022; 2022 Meijia, Chaoyi, Xiaoyan (b0140) 2020; 21 Rayatnia, Khanbabaie (b0180) 2019; 32 Jianjun, Shuying, Angeliki (b0015) 2020; 10 Pfurtscheller (b0025) 1992; 83 Yue, Haiyan (b0155) 2014 Azarmi, Ashtiani, Shalbaf (b0070) 2019; 115 Kim, Ryu, Kim (b0050) 2016; 2016 Heming, Zichao, Chao (b0175) 2022; 37 Xiaoqing, Qingwei, Zhou (b0005) 2016; 42 Schalk, McFarland, Hinterberger, Birbaumer, Wolpaw (b0185) 2004; 51 Marinazzo, Pellicoro, Stramaglia (b0130) 2006; 73 Zhou peng, Dong Chaoyi, Chen Xiaoyan, et al. A Salp Swarm Algorithm Based on Stepped Tent Chaos and Simulated Annealing[J]. Acta Electronica Sinica, 2021, 49(09): 1724-1735. Li Zhanshan, Yang Xinkai, Hu Biao, et al. Differential Evolutionsalp Salp Swarm Feature Selection Algorithm[J]. Journal of Jilin University (Information Science Edition), 2021, 39(01):1-7. Wang, Tao, Cong (b0075) 2020; 8 Mirvaziri, Mobarakeh (b0105) 2017; 32 Mirjalili, Gandomi, Mirjalili (b0145) 2017; 114 Hongtao, Ting, Bezerianos (b0020) 2019; 1 Li, Jingna, Ye (b0065) 2016, 2016. Heming (10.1016/j.bspc.2023.105626_b0170) 2019; 7 Rayatnia (10.1016/j.bspc.2023.105626_b0180) 2019; 32 Wolpaw (10.1016/j.bspc.2023.105626_b0010) 2002; 113 10.1016/j.bspc.2023.105626_b0150 Li (10.1016/j.bspc.2023.105626_b0065) 2016 Setiono (10.1016/j.bspc.2023.105626_b0090) 1997; 8 Meijia (10.1016/j.bspc.2023.105626_b0140) 2020; 21 Junhua (10.1016/j.bspc.2023.105626_b0030) 2013; 23 Wang (10.1016/j.bspc.2023.105626_b0075) 2020; 8 Pfurtscheller (10.1016/j.bspc.2023.105626_b0025) 1992; 83 Marinazzo (10.1016/j.bspc.2023.105626_b0130) 2006; 73 Udhaya Kumar (10.1016/j.bspc.2023.105626_b0115) 2017; 28 Xinxin (10.1016/j.bspc.2023.105626_b0135) 2015; 28 Schalk (10.1016/j.bspc.2023.105626_b0185) 2004; 51 Shuang (10.1016/j.bspc.2023.105626_b0060) 2022; 2022 Xiaoqing (10.1016/j.bspc.2023.105626_b0005) 2016; 42 Aiming (10.1016/j.bspc.2023.105626_b0110) 2017; 17 Heming (10.1016/j.bspc.2023.105626_b0175) 2022; 37 Chakladar (10.1016/j.bspc.2023.105626_b0100) 2020; 60 Lai (10.1016/j.bspc.2023.105626_b0035) 2019 Kim (10.1016/j.bspc.2023.105626_b0050) 2016; 2016 Mirvaziri (10.1016/j.bspc.2023.105626_b0105) 2017; 32 Zhao (10.1016/j.bspc.2023.105626_b0040) 2013 Granger (10.1016/j.bspc.2023.105626_b0125) 1969 10.1016/j.bspc.2023.105626_b0080 Jianjun (10.1016/j.bspc.2023.105626_b0015) 2020; 10 Ocak (10.1016/j.bspc.2023.105626_b0095) 2008; 88 Vikas (10.1016/j.bspc.2023.105626_b0085) 2004; 15 Hongtao (10.1016/j.bspc.2023.105626_b0020) 2019; 1 10.1016/j.bspc.2023.105626_b0165 Yue (10.1016/j.bspc.2023.105626_b0155) 2014 Pengfei (10.1016/j.bspc.2023.105626_b0045) 2022; 371 Minmin (10.1016/j.bspc.2023.105626_b0120) 2018; 30 Dupanloup (10.1016/j.bspc.2023.105626_b0160) 2002; 11 Azarmi (10.1016/j.bspc.2023.105626_b0070) 2019; 115 Mirjalili (10.1016/j.bspc.2023.105626_b0145) 2017; 114 Trad (10.1016/j.bspc.2023.105626_b0055) 2016; 7 |
| References_xml | – volume: 8 year: 1997 ident: b0090 article-title: Neural-network feature selector[J] publication-title: IEEE Trans. Neural Netw. – year: 2019, 2019. ident: b0035 article-title: Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification[J] publication-title: Comput. Intell. Neurosci. – volume: 15 year: 2004 ident: b0085 article-title: Feature selection in MLPs and SVMs based on maximum output information[J] publication-title: IEEE Trans. Neural Netw. – volume: 73 year: 2006 ident: b0130 article-title: Nonlinear parametric model for granger causality of time series[J] publication-title: Physics Review E – volume: 2016 year: 2016 ident: b0050 article-title: Motor imagery classification using mu and beta rhythms of EEG with strong uncorrelating transform based complex common spatial patterns[J] publication-title: Comput. Intell. Neurosci. – volume: 51 start-page: 1034 year: 2004 end-page: 1043 ident: b0185 article-title: BCI2000: A General-Purpose Brain-Computer Interface (BCI) System publication-title: IEEE Trans. Biomed. Eng. – volume: 10 year: 2020 ident: b0015 article-title: Author Correction: Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks.[J] publication-title: Sci. Rep. – start-page: 424 year: 1969: end-page: 438 ident: b0125 article-title: Investigating causal relations by econometric models and cross-spectral methods[J] publication-title: Econometrica – volume: 60 year: 2020 ident: b0100 article-title: EEG-based mental workload estimation using deep BLSTM-LSTM network and evolutionary algorithm[J] publication-title: Biomed. Signal Process. Control – volume: 32 start-page: 1284 year: 2019 end-page: 1289 ident: b0180 article-title: Common spatial patterns feature extraction and support vector machine classification for motor imagery with the secondbrain[J] publication-title: Int. J. Eng. – volume: 7 start-page: 5 year: 2016 end-page: 16 ident: b0055 article-title: Motor imagery signal classification for BCI system using empirical mode decomposition and bandpower feature extraction[J] publication-title: BRAIN. Broad Research in Artificial Intelligence and Neuroscience – volume: 2022 year: 2022 ident: b0060 article-title: A Feature Extraction Algorithm of Brain Network of Motor Imagination Based on a Directed Transfer Function[J] publication-title: Comput. Intell. Neurosci. – volume: 28 start-page: 3239 year: 2017 end-page: 3258 ident: b0115 article-title: PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task[J] publication-title: Neural Comput. & Applic. – volume: 83 start-page: 62 year: 1992 end-page: 69 ident: b0025 article-title: Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest[J] publication-title: Electroencephalogr. Clin. Neurophysiol. – volume: 8 start-page: 155590 year: 2020 end-page: 155601 ident: b0075 article-title: Diverse feature blend based on filter-bank common spatial pattern and brain functional connectivity for multiple motor imagery detection publication-title: IEEE Access – volume: 28 start-page: 178 year: 2015 end-page: 181 ident: b0135 article-title: Development and Limitations of Granger Causality in Neuroscience[J] publication-title: Electronic Sci. & Tech. – volume: 88 start-page: 1858 year: 2008 end-page: 1867 ident: b0095 article-title: Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm[J] publication-title: Signal Process. – volume: 17 year: 2017 ident: b0110 article-title: Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata[J] publication-title: Sensors – year: 2014 ident: b0155 article-title: Chaotic Time Series Prediction for Tent Mapping Based on BP Neural Network Optimized Glowworm Swarm Optimization[J] publication-title: Appl. Mech. Mater. – volume: 42 year: 2016 ident: b0005 article-title: Autoregressive Model Electroencephalogram Signal Identification Based on Feature Selection of Genetic Algorithm[J] publication-title: Comput. Eng. – reference: Brunato M, Battiti R. X-mifs: Exact mutual information for feature selection[C]. //2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016: 3469-3476. – volume: 7 start-page: 71943 year: 2019 end-page: 71962 ident: b0170 article-title: Spotted hyena optimization algorithm with simulated annealing for feature selection[J] publication-title: Ieeeaccess – volume: 11 start-page: 2571 year: 2002 end-page: 2581 ident: b0160 article-title: A simulated annealing approach to define the genetic structure of populations[J] publication-title: Mol. Ecol. – year: 2016, 2016. ident: b0065 article-title: Conditional Granger Causality Analysis of Effective Connectivity during Motor Imagery and Motor Execution in Stroke Patients[J] publication-title: Biomed Res. Int. – volume: 32 start-page: 69 year: 2017 end-page: 75 ident: b0105 article-title: Improvement of EEG-based motor imagery classification using ring topology-based particle swarm optimization[J] publication-title: Biomed. Signal Process. Control – reference: Li Zhanshan, Yang Xinkai, Hu Biao, et al. Differential Evolutionsalp Salp Swarm Feature Selection Algorithm[J]. Journal of Jilin University (Information Science Edition), 2021, 39(01):1-7. – volume: 113 start-page: 767 year: 2002 end-page: 791 ident: b0010 article-title: Brain–computer interfaces for communication and control[J] publication-title: Clin. Neurophysiol. – volume: 115 year: 2019 ident: b0070 article-title: Granger causality analysis in combination with directed network measures for classification of MS patients and healthy controls using task-related fMRI[J] publication-title: Comput. Biol. Med. – volume: 30 year: 2018 ident: b0120 article-title: Application of artificial bee colony algorithm in feature optimization for motor imagery EEG classification[J] publication-title: Neural Comput. & Applic. – volume: 23 start-page: 1350013 year: 2013 ident: b0030 article-title: Design of assistive wheelchair system directly steered by human thoughts[J] publication-title: Int. J. Neural Syst. – year: 2013 ident: b0040 article-title: The Power Spectrum Estimation of the AR Model Based on Motor Imagery EEG[J] publication-title: Adv. Mat. Res. – volume: 21 year: 2020 ident: b0140 article-title: Identifying the pulsed neuron networks' structures by a nonlinear Granger causality method[J] publication-title: BMC Neurosci. – volume: 37 start-page: 445 year: 2022 end-page: 454 ident: b0175 article-title: Simultaneous feature selection optimization based on improved bald eagle search algorithm [J] publication-title: Control and Decision – volume: 371 year: 2022 ident: b0045 article-title: A classification algorithm of an SSVEP brain-Computer interface based on CCA fusion wavelet coefficients[J] publication-title: J. Neurosci. Methods – reference: Zhou peng, Dong Chaoyi, Chen Xiaoyan, et al. A Salp Swarm Algorithm Based on Stepped Tent Chaos and Simulated Annealing[J]. Acta Electronica Sinica, 2021, 49(09): 1724-1735. – volume: 1 start-page: 299 year: 2019 end-page: 309 ident: b0020 article-title: The control of a virtual automatic car based on multiple patterns of motor imagery BCI publication-title: Med. Biol. Eng. Compu. – volume: 114 start-page: 163 year: 2017 end-page: 191 ident: b0145 article-title: Salp swarm algorithm: A bio⁃inspired optimizer for engineering design problems[J] publication-title: Adv. Eng. Softw. – volume: 371 year: 2022 ident: 10.1016/j.bspc.2023.105626_b0045 article-title: A classification algorithm of an SSVEP brain-Computer interface based on CCA fusion wavelet coefficients[J] publication-title: J. Neurosci. Methods – start-page: 424 year: 1969 ident: 10.1016/j.bspc.2023.105626_b0125 article-title: Investigating causal relations by econometric models and cross-spectral methods[J] publication-title: Econometrica doi: 10.2307/1912791 – volume: 23 start-page: 1350013 issue: 03 year: 2013 ident: 10.1016/j.bspc.2023.105626_b0030 article-title: Design of assistive wheelchair system directly steered by human thoughts[J] publication-title: Int. J. Neural Syst. doi: 10.1142/S0129065713500135 – volume: 7 start-page: 71943 year: 2019 ident: 10.1016/j.bspc.2023.105626_b0170 article-title: Spotted hyena optimization algorithm with simulated annealing for feature selection[J] publication-title: Ieeeaccess – ident: 10.1016/j.bspc.2023.105626_b0080 doi: 10.1109/IJCNN.2016.7727644 – volume: 8 start-page: 155590 year: 2020 ident: 10.1016/j.bspc.2023.105626_b0075 article-title: Diverse feature blend based on filter-bank common spatial pattern and brain functional connectivity for multiple motor imagery detection publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3018962 – volume: 32 start-page: 69 year: 2017 ident: 10.1016/j.bspc.2023.105626_b0105 article-title: Improvement of EEG-based motor imagery classification using ring topology-based particle swarm optimization[J] publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2016.10.015 – ident: 10.1016/j.bspc.2023.105626_b0150 – volume: 37 start-page: 445 issue: 02 year: 2022 ident: 10.1016/j.bspc.2023.105626_b0175 article-title: Simultaneous feature selection optimization based on improved bald eagle search algorithm [J] publication-title: Control and Decision – volume: 1 start-page: 299 year: 2019 ident: 10.1016/j.bspc.2023.105626_b0020 article-title: The control of a virtual automatic car based on multiple patterns of motor imagery BCI publication-title: Med. Biol. Eng. Compu. – volume: 113 start-page: 767 issue: 6 year: 2002 ident: 10.1016/j.bspc.2023.105626_b0010 article-title: Brain–computer interfaces for communication and control[J] publication-title: Clin. Neurophysiol. doi: 10.1016/S1388-2457(02)00057-3 – volume: 15 issue: 4 year: 2004 ident: 10.1016/j.bspc.2023.105626_b0085 article-title: Feature selection in MLPs and SVMs based on maximum output information[J] publication-title: IEEE Trans. Neural Netw. – volume: 32 start-page: 1284 issue: 9 year: 2019 ident: 10.1016/j.bspc.2023.105626_b0180 article-title: Common spatial patterns feature extraction and support vector machine classification for motor imagery with the secondbrain[J] publication-title: Int. J. Eng. – year: 2013 ident: 10.1016/j.bspc.2023.105626_b0040 article-title: The Power Spectrum Estimation of the AR Model Based on Motor Imagery EEG[J] publication-title: Adv. Mat. Res. – volume: 51 start-page: 1034 issue: 6 year: 2004 ident: 10.1016/j.bspc.2023.105626_b0185 article-title: BCI2000: A General-Purpose Brain-Computer Interface (BCI) System publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2004.827072 – volume: 60 year: 2020 ident: 10.1016/j.bspc.2023.105626_b0100 article-title: EEG-based mental workload estimation using deep BLSTM-LSTM network and evolutionary algorithm[J] publication-title: Biomed. Signal Process. Control – volume: 17 issue: 11 year: 2017 ident: 10.1016/j.bspc.2023.105626_b0110 article-title: Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata[J] publication-title: Sensors – volume: 42 issue: 03 year: 2016 ident: 10.1016/j.bspc.2023.105626_b0005 article-title: Autoregressive Model Electroencephalogram Signal Identification Based on Feature Selection of Genetic Algorithm[J] publication-title: Comput. Eng. – volume: 115 year: 2019 ident: 10.1016/j.bspc.2023.105626_b0070 article-title: Granger causality analysis in combination with directed network measures for classification of MS patients and healthy controls using task-related fMRI[J] publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2019.103495 – year: 2014 ident: 10.1016/j.bspc.2023.105626_b0155 article-title: Chaotic Time Series Prediction for Tent Mapping Based on BP Neural Network Optimized Glowworm Swarm Optimization[J] publication-title: Appl. Mech. Mater. – volume: 73 year: 2006 ident: 10.1016/j.bspc.2023.105626_b0130 article-title: Nonlinear parametric model for granger causality of time series[J] publication-title: Physics Review E doi: 10.1103/PhysRevE.73.066216 – volume: 21 issue: 1 year: 2020 ident: 10.1016/j.bspc.2023.105626_b0140 article-title: Identifying the pulsed neuron networks' structures by a nonlinear Granger causality method[J] publication-title: BMC Neurosci. – volume: 83 start-page: 62 issue: 1 year: 1992 ident: 10.1016/j.bspc.2023.105626_b0025 article-title: Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest[J] publication-title: Electroencephalogr. Clin. Neurophysiol. doi: 10.1016/0013-4694(92)90133-3 – volume: 30 issue: 12 year: 2018 ident: 10.1016/j.bspc.2023.105626_b0120 article-title: Application of artificial bee colony algorithm in feature optimization for motor imagery EEG classification[J] publication-title: Neural Comput. & Applic. – volume: 88 start-page: 1858 issue: 7 year: 2008 ident: 10.1016/j.bspc.2023.105626_b0095 article-title: Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm[J] publication-title: Signal Process. doi: 10.1016/j.sigpro.2008.01.026 – volume: 8 issue: 3 year: 1997 ident: 10.1016/j.bspc.2023.105626_b0090 article-title: Neural-network feature selector[J] publication-title: IEEE Trans. Neural Netw. – volume: 11 start-page: 2571 issue: 12 year: 2002 ident: 10.1016/j.bspc.2023.105626_b0160 article-title: A simulated annealing approach to define the genetic structure of populations[J] publication-title: Mol. Ecol. doi: 10.1046/j.1365-294X.2002.01650.x – volume: 10 issue: 1 year: 2020 ident: 10.1016/j.bspc.2023.105626_b0015 article-title: Author Correction: Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks.[J] publication-title: Sci. Rep. – volume: 114 start-page: 163 issue: 6 year: 2017 ident: 10.1016/j.bspc.2023.105626_b0145 article-title: Salp swarm algorithm: A bio⁃inspired optimizer for engineering design problems[J] publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2017.07.002 – volume: 28 start-page: 3239 issue: 11 year: 2017 ident: 10.1016/j.bspc.2023.105626_b0115 article-title: PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task[J] publication-title: Neural Comput. & Applic. doi: 10.1007/s00521-016-2236-5 – ident: 10.1016/j.bspc.2023.105626_b0165 – year: 2019 ident: 10.1016/j.bspc.2023.105626_b0035 article-title: Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification[J] publication-title: Comput. Intell. Neurosci. doi: 10.1155/2019/7895924 – volume: 2022 year: 2022 ident: 10.1016/j.bspc.2023.105626_b0060 article-title: A Feature Extraction Algorithm of Brain Network of Motor Imagination Based on a Directed Transfer Function[J] publication-title: Comput. Intell. Neurosci. – volume: 28 start-page: 178 issue: 08 year: 2015 ident: 10.1016/j.bspc.2023.105626_b0135 article-title: Development and Limitations of Granger Causality in Neuroscience[J] publication-title: Electronic Sci. & Tech. – volume: 2016 year: 2016 ident: 10.1016/j.bspc.2023.105626_b0050 article-title: Motor imagery classification using mu and beta rhythms of EEG with strong uncorrelating transform based complex common spatial patterns[J] publication-title: Comput. Intell. Neurosci. doi: 10.1155/2016/1489692 – volume: 7 start-page: 5 issue: 2 year: 2016 ident: 10.1016/j.bspc.2023.105626_b0055 article-title: Motor imagery signal classification for BCI system using empirical mode decomposition and bandpower feature extraction[J] publication-title: BRAIN. Broad Research in Artificial Intelligence and Neuroscience – year: 2016 ident: 10.1016/j.bspc.2023.105626_b0065 article-title: Conditional Granger Causality Analysis of Effective Connectivity during Motor Imagery and Motor Execution in Stroke Patients[J] publication-title: Biomed Res. Int. |
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