ENIC: Ensemble and Nature Inclined Classification with Sparse Depiction based Deep and Transfer Learning for Biosignal Classification
The electrical activities of the brain are recorded and measured with Electroencephalography (EEG) by means of placing the electrodes on the scalp of the brain. It is quite a famous and versatile methodology utilized in both clinical and academic research activities. In this work, sparse depiction i...
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| Veröffentlicht in: | Applied soft computing Jg. 117; S. 108416 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.03.2022
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | The electrical activities of the brain are recorded and measured with Electroencephalography (EEG) by means of placing the electrodes on the scalp of the brain. It is quite a famous and versatile methodology utilized in both clinical and academic research activities. In this work, sparse depiction is initially incorporated to the EEG signals by means of using K-Singular Value Decomposition (K-SVD) algorithm and the features are extracted by means of using Self-Organizing Map (SOM) technique. The extracted features are initially classified with Extreme Learning Machine (ELM) and the proposed classification versions of ELM such as Ensemble ELM model and Nature Inclined ELM Model. The proposed ensemble ELM model makes use of the combination of Modified Adaboost. RT based on wavelet thresholding with ELM. The proposed Nature Inclined ELM makes use of the combination of some famous swarm intelligence algorithms such as Genetic Algorithm based ELM (GA-ELM), Particle Swarm Optimization based ELM (PSO-ELM), Ant Colony Optimization based ELM (ACO-ELM), Artificial Bee Colony based ELM (ABC-ELM) and Glowworm Swarm Optimization based ELM (GSO-ELM). The extracted features are also classified with deep learning methodology by means of utilizing an incidental Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN). Another famous methodology using Non-negative Matrix Factorization (NMF) and Affinity Propagation Congregation based Mutual Information (APCMI) with transfer learning techniques is also proposed and implemented once the sparse modelling is done and the results are analysed. The proposed methodology is implemented for two EEG datasets such as epilepsy dataset and schizophrenia dataset and a comprehensive analysis is done with very promising results.
•Sparse Depiction is imposed to EEG Signals initially.•K-SVD algorithm and Self-Organizing Map technique is utilized.•An Ensemble and Nature Inclined Classification model with ELM is proposed.•A transfer learning model along with deep learning model is also implemented.•The developed models are tested on two EEG signal datasets. |
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| AbstractList | The electrical activities of the brain are recorded and measured with Electroencephalography (EEG) by means of placing the electrodes on the scalp of the brain. It is quite a famous and versatile methodology utilized in both clinical and academic research activities. In this work, sparse depiction is initially incorporated to the EEG signals by means of using K-Singular Value Decomposition (K-SVD) algorithm and the features are extracted by means of using Self-Organizing Map (SOM) technique. The extracted features are initially classified with Extreme Learning Machine (ELM) and the proposed classification versions of ELM such as Ensemble ELM model and Nature Inclined ELM Model. The proposed ensemble ELM model makes use of the combination of Modified Adaboost. RT based on wavelet thresholding with ELM. The proposed Nature Inclined ELM makes use of the combination of some famous swarm intelligence algorithms such as Genetic Algorithm based ELM (GA-ELM), Particle Swarm Optimization based ELM (PSO-ELM), Ant Colony Optimization based ELM (ACO-ELM), Artificial Bee Colony based ELM (ABC-ELM) and Glowworm Swarm Optimization based ELM (GSO-ELM). The extracted features are also classified with deep learning methodology by means of utilizing an incidental Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN). Another famous methodology using Non-negative Matrix Factorization (NMF) and Affinity Propagation Congregation based Mutual Information (APCMI) with transfer learning techniques is also proposed and implemented once the sparse modelling is done and the results are analysed. The proposed methodology is implemented for two EEG datasets such as epilepsy dataset and schizophrenia dataset and a comprehensive analysis is done with very promising results.
•Sparse Depiction is imposed to EEG Signals initially.•K-SVD algorithm and Self-Organizing Map technique is utilized.•An Ensemble and Nature Inclined Classification model with ELM is proposed.•A transfer learning model along with deep learning model is also implemented.•The developed models are tested on two EEG signal datasets. |
| ArticleNumber | 108416 |
| Author | Prabhakar, Sunil Kumar Lee, Seong-Whan |
| Author_xml | – sequence: 1 givenname: Sunil Kumar orcidid: 0000-0003-4019-2345 surname: Prabhakar fullname: Prabhakar, Sunil Kumar – sequence: 2 givenname: Seong-Whan surname: Lee fullname: Lee, Seong-Whan email: sw.lee@korea.ac.kr |
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| Cites_doi | 10.1109/TNSRE.2017.2748388 10.1371/journal.pone.0188629 10.1016/j.eswa.2009.09.051 10.1109/TKDE.2009.191 10.1016/j.procs.2016.07.422 10.1155/2019/1806314 10.1016/j.bspc.2011.07.007 10.1155/2015/129021 10.1155/2020/8853835 10.1109/TIFS.2016.2577551 10.1016/j.cmpb.2016.08.013 10.1007/s11760-012-0362-9 10.1177/0954411920966937 10.1007/s00521-013-1522-8 10.1002/ima.22486 10.1016/j.bspc.2021.102936 10.3390/ijerph16040599 10.1016/j.engappai.2010.06.009 10.1186/s40708-020-00105-1 10.1103/PhysRevE.64.061907 10.1007/s11045-016-0389-0 10.1016/j.bbe.2019.12.002 10.1155/2014/426152 10.1016/j.eswa.2011.07.048 10.1109/78.650093 10.3390/brainsci9120348 10.2174/1573405615666190404163233 10.5370/JEET.2016.11.4.993 10.1142/S012906572150026X 10.3390/app7101060 10.1002/ima.20283 10.1109/TNSRE.2018.2839116 10.1016/j.patcog.2015.03.010 10.3389/fnagi.2016.00092 10.1155/2015/405890 10.1007/978-3-319-28397-5_28 10.1016/j.eswa.2018.04.021 10.1109/ACCESS.2020.3011140 10.1109/TITB.2006.884369 10.1142/S0129065706000482 10.1155/2019/8719387 10.1109/TSP.2013.2250968 10.1109/TIM.2009.2026612 10.1155/2008/361705 10.1155/2014/813197 10.1007/s00521-012-1158-0 10.1109/IJCNN.2004.1380068 10.1109/TCYB.2015.2399420 10.1155/2014/627892 10.1007/s11517-018-1875-3 10.1007/s11042-021-10597-6 10.3389/fnins.2020.00808 10.1088/1741-2560/9/5/056002 10.1109/TIP.2010.2081678 10.1155/2020/8206245 10.1016/j.eswa.2011.07.008 10.1007/s11517-015-1351-2 10.1016/j.compbiomed.2019.01.013 10.1109/TNNLS.2019.2946869 10.1016/j.jbi.2018.05.007 10.1016/j.heliyon.2020.e05689 10.1016/j.bbe.2020.05.008 10.24251/HICSS.2020.393 10.1016/j.patcog.2019.01.015 10.18280/ts.370209 10.1109/ACCESS.2020.2975848 10.1155/2015/103796 10.1109/34.879789 10.3390/electronics9050811 10.1155/2020/2918276 10.1155/2018/9593682 10.1109/CBMS.2012.6266371 10.1016/j.eswa.2019.03.021 10.1016/j.neunet.2020.01.017 |
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| References | Ding, Xuand, Nie (b29) 2014; 25 Ullah, Hussain, Qazi, Aboalsamh (b53) 2018; 107 Aayesha, Qureshi, Afzaal (b17) 2021; 80 Andrzejak, Lehnertz, Rieke, Mormann, Elger (b84) 2001; 64 She, Chen, Ma, Nguyen, Zhang (b46) 2018; 2018 Acharya, Molinari, Vinitha, Chattopadhyay (b90) 2012; 7 G.B. Huang, Q.Y. Zhu, C.K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in: Proceedings of the IEEE International Joint Conference on Neural Networks, Vol. 2, 2004, pp. 985–990. Krishnan, Raj, Balasubramanian, Chen (b21) 2020 Michielli, Acharya, Molinari (b80) 2019; 106 Lee, Fazli, Mehnert, Lee (b51) 2015; 48 Krishnan, Ghose (b76) 2011 So, Madusanka, Choi, Choi, Park (b55) 2019; 15 Yildirim, Baloglu, Acharya (b56) 2019; 16 Chu, Lin, Yang, Diao, Zhang, Zhang, Fan, Shen, Yan (b39) 2019; 2019 Ultsch, Siemon (b65) 1990 Chen, Atnafu, Schlattner, Weldtsadik, Roh, Kim, Lee, Blankertz, Fazli (b4) 2016; 11 Lee, Williamson, Won, Fazli, Lee (b2) 2018; 26 Lu (b41) 2016; 46 Shoeibi, Ghassemi, Khodatars (b14) 2020 A. Graves, Generating sequences with recurrent neural networks, 0000. Yaghoobi, Nam, Gribonval, Davies (b64) 2013; 61 Nkengfack, Tchiotsop, Atangana, Door, Wolf (b16) 2021; 23 Zhao, Guo, Wang, Li, Pang, Georgakopoulos (b43) 2015 Yang, Li (b12) 2009; 59 Murugavel, Ramakrishnan (b48) 2016; 54 Foithong, Pinngern, Attachoo (b82) 2012; 39 Raghu, Sriraam, Temel, Rao, Kubben (b62) 2020; 124 Schmidt, Laurberg (b81) 2008; 2008 Raghu, Sriraam, Pradeep (b92) 2017 Elad, Aharon (b11) 2006 Sharma, Omlin (b68) 2009; 5 Shalbaf, Bagherzadeh, Maghsoudi (b23) 2020 Aslan, Akin (b26) 2020; 37 . Yu, Qiao, Chen, Lee, Fei, Shen (b5) 2019; 90 Karaboga, Akay (b75) 2009; 214 Prabhakar, Rajaguru (b69) 2020; 6 Singh, Singh, Malhotra (b22) 2021; 235 Prabhakar, Rajaguru, Lee (b25) 2020; 8 Nicolaou, Georgiou (b86) 2012; 39 Jammoussi, Nasr (b31) 2020; 2020 Kumar, Dewal, Anand (b91) 2014; 8 Lee, Jeong, Shim, Lee (b60) 2020 Yang, Zhou, Xie, Ding, Yang, Zhang (b67) 2011; 20 R. Buettner, D. Beil, S. Scholtz, A. Djemai, Development of a machine learning based algorithm to accurately detect schizophrenia based on one-minute EEG recordings, in: Proceedings of the 53rd Hawaii International Conference on System Sciences, 2020. Liang (b44) 2006; 16 Suresh, Saraswathi, Sundararajan (b38) 2010; 23 Han, Kwak, Oh, Lee (b57) 2020; 40 Rajaguru, Thangavel (b70) 2014; 2014 Mao, Zhang, Liu, Li, Yang (b30) 2014; 2014 Ding, Zhang, Xu, Guo, Zhang (b49) 2015; 2015 Wang, Cai, Peng, Jia (b35) 2015; 2015 Huang, Li, Chen, Lin, Yao (b10) 2020; 14 Jiang (b61) 2017; 25 Lee, Jeong, Lee (b50) 2020; 8 Srinath, Gayathri (b19) 2021; 31 Lee, Lewicki, Sejnowski (b66) 2000; 22 Prabhakar, Rajaguru, Kim (b24) 2020; 2020 Zhu, Suk, Lee, Shen (b6) 2017 Pan, Yang (b59) 2010; 22 Wang, Shi, Zhang, Zhu (b83) 2021; 2021 She, Hu, Luo (b45) 2019; 57 Jeong, Yu, Lee, Lee (b58) 2019; 9 Liu, Dai, Wang (b73) 2008; 34 Zhou, Tan, Wen, Sun, Han, Xu (b9) 2016; 8 Wei, Liu, Yan, Sun (b71) 2016; 28 Manivasagam (b40) 2016; 46 L. Duan, M. Bao, J. Miao, Y. Xu, J. Chen, Classification Based on Multilayer Extreme Learning Machine for Motor Imagery Task from EEG Signals, Procedia Comput. Sci. (ISSN: 1877-0509) 88, 176–184 Li, Huang, Zhou, Zhong (b79) 2017; 7 Kwon, Lee, Guan, Lee (b3) 2020; 31 S. Xie, S. Krishnan, A.T. Lawniczak, Sparse principal component extraction and classification of long-term biomedical signals, in: 25th International Symposium on Computer-Based Medical Systems, Rome, 2012, pp. 1–6. Schuster, Paliwal (b77) 1997; 45 Siddiqui, Menendez, Huang, Hussain (b13) 2020; 7 Wei, Huang, Chen, Zheng, Wang (b34) 2019; 2019 Shin, Lee, Lee, Lee (b8) 2012; 9 Suk, Lee (b1) 2011; 21 Ozdemir, Cura, Akan (b18) 2021 Olejarczyk, Jernajczyk (b85) 2017; 12 Cao, Lin (b32) 2015; 2015 He, Cao (b54) 2018; 83 Liu, Xiao, Xu, Cai (b15) 2021; 20 Avci (b37) 2016; 11 Hassan, Subasi (b87) 2016; 136 Srinivasan, Eswaran, Sriraam (b88) 2007; 11 Yaseen, Faris, Ansari (b36) 2020; 2020 Eshelman, Schaffer (b72) 1991 Souza (b33) 2020; 9 Raghu, Sriraam, Hegde, Kubben (b93) 2019; 127 L. Duan, Y. Xu, S. Cui, J. Chen, M. Bao, Feature extraction of motor imagery eeg based on extreme learning machine auto encoder, in: Proceedings of ELM-2015, Vol. 1. Hoyer (b63) 2004; 5 Baygin, Yaman, Tuncer, Dogan, Barua, Acharya (b20) 2021; 70 Salahi, Jamalian, Taati (b74) 2013; 23 Jirayucharoensak, Ngum, Israsena (b52) 2014; 2014 Pravin, Sriraam, Benakop, Jinaga (b89) 2010; 37 Han (10.1016/j.asoc.2022.108416_b57) 2020; 40 Nkengfack (10.1016/j.asoc.2022.108416_b16) 2021; 23 Elad (10.1016/j.asoc.2022.108416_b11) 2006 Shalbaf (10.1016/j.asoc.2022.108416_b23) 2020 Lee (10.1016/j.asoc.2022.108416_b51) 2015; 48 Chu (10.1016/j.asoc.2022.108416_b39) 2019; 2019 Wang (10.1016/j.asoc.2022.108416_b35) 2015; 2015 Lu (10.1016/j.asoc.2022.108416_b41) 2016; 46 Foithong (10.1016/j.asoc.2022.108416_b82) 2012; 39 Ultsch (10.1016/j.asoc.2022.108416_b65) 1990 10.1016/j.asoc.2022.108416_b78 Yaghoobi (10.1016/j.asoc.2022.108416_b64) 2013; 61 Srinivasan (10.1016/j.asoc.2022.108416_b88) 2007; 11 Siddiqui (10.1016/j.asoc.2022.108416_b13) 2020; 7 Wang (10.1016/j.asoc.2022.108416_b83) 2021; 2021 Wei (10.1016/j.asoc.2022.108416_b34) 2019; 2019 Raghu (10.1016/j.asoc.2022.108416_b93) 2019; 127 Yildirim (10.1016/j.asoc.2022.108416_b56) 2019; 16 Prabhakar (10.1016/j.asoc.2022.108416_b25) 2020; 8 Murugavel (10.1016/j.asoc.2022.108416_b48) 2016; 54 Sharma (10.1016/j.asoc.2022.108416_b68) 2009; 5 Pravin (10.1016/j.asoc.2022.108416_b89) 2010; 37 Ding (10.1016/j.asoc.2022.108416_b29) 2014; 25 Lee (10.1016/j.asoc.2022.108416_b66) 2000; 22 Raghu (10.1016/j.asoc.2022.108416_b62) 2020; 124 Lee (10.1016/j.asoc.2022.108416_b50) 2020; 8 Ding (10.1016/j.asoc.2022.108416_b49) 2015; 2015 Huang (10.1016/j.asoc.2022.108416_b10) 2020; 14 Prabhakar (10.1016/j.asoc.2022.108416_b24) 2020; 2020 Ullah (10.1016/j.asoc.2022.108416_b53) 2018; 107 Lee (10.1016/j.asoc.2022.108416_b60) 2020 Liu (10.1016/j.asoc.2022.108416_b15) 2021; 20 Aayesha (10.1016/j.asoc.2022.108416_b17) 2021; 80 Shoeibi (10.1016/j.asoc.2022.108416_b14) 2020 Manivasagam (10.1016/j.asoc.2022.108416_b40) 2016; 46 Liu (10.1016/j.asoc.2022.108416_b73) 2008; 34 Yu (10.1016/j.asoc.2022.108416_b5) 2019; 90 She (10.1016/j.asoc.2022.108416_b46) 2018; 2018 Eshelman (10.1016/j.asoc.2022.108416_b72) 1991 Schuster (10.1016/j.asoc.2022.108416_b77) 1997; 45 Srinath (10.1016/j.asoc.2022.108416_b19) 2021; 31 10.1016/j.asoc.2022.108416_b27 10.1016/j.asoc.2022.108416_b28 Schmidt (10.1016/j.asoc.2022.108416_b81) 2008; 2008 Lee (10.1016/j.asoc.2022.108416_b2) 2018; 26 Ozdemir (10.1016/j.asoc.2022.108416_b18) 2021 Pan (10.1016/j.asoc.2022.108416_b59) 2010; 22 He (10.1016/j.asoc.2022.108416_b54) 2018; 83 Souza (10.1016/j.asoc.2022.108416_b33) 2020; 9 Cao (10.1016/j.asoc.2022.108416_b32) 2015; 2015 Acharya (10.1016/j.asoc.2022.108416_b90) 2012; 7 Baygin (10.1016/j.asoc.2022.108416_b20) 2021; 70 Prabhakar (10.1016/j.asoc.2022.108416_b69) 2020; 6 Zhao (10.1016/j.asoc.2022.108416_b43) 2015 Andrzejak (10.1016/j.asoc.2022.108416_b84) 2001; 64 Hoyer (10.1016/j.asoc.2022.108416_b63) 2004; 5 Liang (10.1016/j.asoc.2022.108416_b44) 2006; 16 Zhu (10.1016/j.asoc.2022.108416_b6) 2017 Aslan (10.1016/j.asoc.2022.108416_b26) 2020; 37 So (10.1016/j.asoc.2022.108416_b55) 2019; 15 Krishnan (10.1016/j.asoc.2022.108416_b76) 2011 Yang (10.1016/j.asoc.2022.108416_b12) 2009; 59 Li (10.1016/j.asoc.2022.108416_b79) 2017; 7 Wei (10.1016/j.asoc.2022.108416_b71) 2016; 28 She (10.1016/j.asoc.2022.108416_b45) 2019; 57 Olejarczyk (10.1016/j.asoc.2022.108416_b85) 2017; 12 Rajaguru (10.1016/j.asoc.2022.108416_b70) 2014; 2014 Raghu (10.1016/j.asoc.2022.108416_b92) 2017 Avci (10.1016/j.asoc.2022.108416_b37) 2016; 11 Kwon (10.1016/j.asoc.2022.108416_b3) 2020; 31 Mao (10.1016/j.asoc.2022.108416_b30) 2014; 2014 Hassan (10.1016/j.asoc.2022.108416_b87) 2016; 136 10.1016/j.asoc.2022.108416_b7 Singh (10.1016/j.asoc.2022.108416_b22) 2021; 235 Suresh (10.1016/j.asoc.2022.108416_b38) 2010; 23 Salahi (10.1016/j.asoc.2022.108416_b74) 2013; 23 Suk (10.1016/j.asoc.2022.108416_b1) 2011; 21 10.1016/j.asoc.2022.108416_b47 Michielli (10.1016/j.asoc.2022.108416_b80) 2019; 106 Zhou (10.1016/j.asoc.2022.108416_b9) 2016; 8 Yaseen (10.1016/j.asoc.2022.108416_b36) 2020; 2020 Yang (10.1016/j.asoc.2022.108416_b67) 2011; 20 Karaboga (10.1016/j.asoc.2022.108416_b75) 2009; 214 Jiang (10.1016/j.asoc.2022.108416_b61) 2017; 25 10.1016/j.asoc.2022.108416_b42 Kumar (10.1016/j.asoc.2022.108416_b91) 2014; 8 Nicolaou (10.1016/j.asoc.2022.108416_b86) 2012; 39 Shin (10.1016/j.asoc.2022.108416_b8) 2012; 9 Chen (10.1016/j.asoc.2022.108416_b4) 2016; 11 Jammoussi (10.1016/j.asoc.2022.108416_b31) 2020; 2020 Jirayucharoensak (10.1016/j.asoc.2022.108416_b52) 2014; 2014 Jeong (10.1016/j.asoc.2022.108416_b58) 2019; 9 Krishnan (10.1016/j.asoc.2022.108416_b21) 2020 |
| References_xml | – volume: 23 year: 2021 ident: b16 article-title: Classification of EEG signals for epileptic seizures detection and eye states identification using Jacobi polynomial transforms-based measures of complexity and least-squares support vector machines publication-title: Inform. Med. Unlocked – volume: 5 start-page: 1 year: 2009 end-page: 12 ident: b68 article-title: Performance comparison of particle swarm optimization with traditional clustering algorithms used in self organizing map publication-title: Int. J. Comput. Intell. – year: 2020 ident: b14 article-title: Application of deep learning techniques for automated detection of epileptic seizures: A review – volume: 2015 year: 2015 ident: b49 article-title: Deep extreme learning machine and its application in EEG classification publication-title: Math. Probl. Eng. – volume: 127 start-page: 323 year: 2019 end-page: 341 ident: b93 article-title: A novel approach for classification of epileptic seizures using matrix determinant publication-title: Expert Syst. Appl. – volume: 235 start-page: 167 year: 2021 end-page: 184 ident: b22 article-title: Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients publication-title: Proc. Inst. Mech. Eng. [H] – volume: 57 start-page: 147 year: 2019 end-page: 157 ident: b45 article-title: A hierarchical semi-supervised extreme learning machine method for EEG recognition publication-title: Med. Biol. Eng. Comput. – volume: 21 start-page: 123 year: 2011 end-page: 130 ident: b1 article-title: Subject and class specific frequency bands selection for multi-class motor imagery classification publication-title: Int. J. Imaging Syst. Technol. – volume: 70 year: 2021 ident: b20 article-title: Automated accurate schizophrenia detection system using collatz pattern technique with EEG signals’ publication-title: Biomed. Signal Process. Control – volume: 8 start-page: 1323 year: 2014 end-page: 1334 ident: b91 article-title: Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network publication-title: SiViP – volume: 7 start-page: 401 year: 2012 end-page: 408 ident: b90 article-title: Automatic diagnosis of epileptic EEG using entropies publication-title: Biomed. Signal Process. Control – volume: 39 start-page: 202 year: 2012 end-page: 209 ident: b86 article-title: Detection of epileptic electroencephalogram based on perumutation entropy and support vector machine publication-title: Expert Syst. Appl. – volume: 2020 year: 2020 ident: b31 article-title: A hybrid method based on extreme learning machine and self organizing map for pattern classification publication-title: Comput. Intell. Neurosci. – volume: 16 start-page: 29 year: 2006 end-page: 38 ident: b44 article-title: Classification of mental tasks from EEG signals using extreme learning machine publication-title: Int. J. Neural Syst. – volume: 107 start-page: 61 year: 2018 end-page: 71 ident: b53 article-title: An automated system for epilepsy detection using EEG brain signals based on deep learning approach publication-title: Expert Syst. Appl. – volume: 34 start-page: 208 year: 2008 end-page: 210 ident: b73 article-title: Ant colony algorithm parameters optimization publication-title: Comput. Eng. – reference: S. Xie, S. Krishnan, A.T. Lawniczak, Sparse principal component extraction and classification of long-term biomedical signals, in: 25th International Symposium on Computer-Based Medical Systems, Rome, 2012, pp. 1–6. – volume: 25 start-page: 549 year: 2014 end-page: 556 ident: b29 article-title: Extreme learning machine publication-title: Neural Comput. Appl. – volume: 11 start-page: 993 year: 2016 end-page: 1002 ident: b37 article-title: An automatic diagnosis system for hepatitis diseases based on genetic wavelet kernel extreme learning machine publication-title: J. Electr. Eng. Technol. – volume: 59 start-page: 884 year: 2009 end-page: 892 ident: b12 article-title: Multifocus image fusion and restoration with sparse representation publication-title: IEEE Trans. Instrum. Meas. – volume: 80 start-page: 17849 year: 2021 end-page: 17877 ident: b17 article-title: Machine learning-based EEG signals classification model for epileptic seizure detection publication-title: Multimedia Tools Appl. – volume: 11 start-page: 288 year: 2007 end-page: 295 ident: b88 article-title: Approximate entropy-based epileptic EEG detection using artificial neural networks publication-title: IEEE Trans. Inf. Technol. Biomed. – volume: 8 start-page: 134524 year: 2020 end-page: 134535 ident: b50 article-title: SessionNet: FEature similarity-based weighted ensemble learning for motor imagery classification publication-title: IEEE Access – volume: 83 start-page: 103 year: 2018 end-page: 111 ident: b54 article-title: Automated depression analysis using convolutional neural networks from speech publication-title: J. Biomed. Inform. – volume: 22 start-page: 1078 year: 2000 end-page: 1089 ident: b66 article-title: ICA Mixture models for unsupervised classification of non-Gaussian classes and automatic context switching in blind signal separation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 51 year: 2017 end-page: 66 ident: b92 article-title: Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent elman neural network classifier publication-title: Cognitive Neurodynamics, Vol. 11 – reference: R. Buettner, D. Beil, S. Scholtz, A. Djemai, Development of a machine learning based algorithm to accurately detect schizophrenia based on one-minute EEG recordings, in: Proceedings of the 53rd Hawaii International Conference on System Sciences, 2020. – start-page: 9 year: 2015 ident: b43 article-title: Analyze EEG signals with extreme learning machine based on PMIS feature selection publication-title: Int. J. Mach. Learn. Cybern. – start-page: 115 year: 1991 end-page: 122 ident: b72 article-title: Preventing premature convergence in genetic algorithms by preventing incest publication-title: Proceedings of the 4th International Conference on Genetic Algorithms (ICGA ’91) – volume: 20 year: 2021 ident: b15 article-title: Minireview of epilepsy detection techniques based on electroencephalogram signals publication-title: Front. Syst. Neurosci. – volume: 37 start-page: 3284 year: 2010 end-page: 3291 ident: b89 article-title: Entropies based detection of epileptic seizures with artificial neural network classifiers publication-title: Expert Syst. Appl. – volume: 9 start-page: 811 year: 2020 ident: b33 article-title: An advanced pruning method in the architecture of extreme learning machines using L1 regularization and bootstrapping publication-title: Electronics – start-page: 895 year: 2006 end-page: 900 ident: b11 article-title: Image denoising via learned dictionaries and sparse representation publication-title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), Vol. 1 – volume: 46 year: 2016 ident: b40 article-title: Fault detection in state variable filter circuit using kernel extreme learning machine (KELM) algorithm publication-title: Inform. MIDEM – reference: L. Duan, M. Bao, J. Miao, Y. Xu, J. Chen, Classification Based on Multilayer Extreme Learning Machine for Motor Imagery Task from EEG Signals, Procedia Comput. Sci. (ISSN: 1877-0509) 88, 176–184, – volume: 5 start-page: 1457 year: 2004 end-page: 1469 ident: b63 article-title: Non-negative matrix factorization with sparseness constraints publication-title: J. Mach. Learn. Res. – year: 2021 ident: b18 article-title: Epileptic EEG classification by using time-frequency images for deep learning publication-title: Int. J. Neural Syst. – volume: 136 start-page: 65 year: 2016 end-page: 77 ident: b87 article-title: Automatic identification of epileptic seizures from EEG signals using linear programming boosting publication-title: Comput. Methods Programs Biomed. – volume: 214 start-page: 108 year: 2009 end-page: 132 ident: b75 article-title: A comparative study of artificial bee colony algorithm publication-title: Appl. Math. Comput. – volume: 2021 year: 2021 ident: b83 article-title: An affinity propagation-based clustering method for the temporal dynamics management of high-speed railway passenger demand publication-title: J. Adv. Transp. – start-page: 1354 year: 2020 end-page: 1358 ident: b60 article-title: Decoding movement imagination and execution from eeg signals using bci-transfer learning method based on relation network publication-title: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) – volume: 64 year: 2001 ident: b84 article-title: Indications of non linear deterministic and finite dimensional structures in time series of brain electrical activity: dependence on recording region and brain state publication-title: Phys. Rev. E – volume: 124 start-page: 202 year: 2020 end-page: 212 ident: b62 article-title: EEG Based multi-class seizure type classification using convolutional neural network and transfer learning publication-title: Neural Netw. – volume: 2015 year: 2015 ident: b35 article-title: A novel multiple instance learning method based on extreme learning machine publication-title: Comput. Intell. Neurosci. – start-page: 451 year: 2011 end-page: 467 ident: b76 article-title: Glowworm swarm optimization for multimodal search spaces publication-title: Handbook of Swarm Intelligence – volume: 12 year: 2017 ident: b85 article-title: Graph-based analysis of brain connectivity in schizophrenia publication-title: PLoS One – volume: 25 start-page: 2270 year: 2017 end-page: 2284 ident: b61 article-title: Seizure classification from EEG signals using transfer learning, semi-supervised learning and TSK fuzzy system publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 46 year: 2016 ident: b41 article-title: Robust extreme learning machine with its application to indoor positioning publication-title: IEEE Trans. Cybern. – volume: 61 start-page: 2341 year: 2013 end-page: 2355 ident: b64 article-title: Constrained overcomplete analysis operator learning for cosparse signal modelling publication-title: IEEE Trans. Signal Process. – volume: 106 start-page: 71 year: 2019 end-page: 81 ident: b80 article-title: Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals publication-title: Comput. Biol. Med. – volume: 2020 start-page: 1 year: 2020 end-page: 14 ident: b24 article-title: Schizophrenia EEG signal classification based on swarm intelligence computing publication-title: Comput. Intell. Neurosci. – reference: L. Duan, Y. Xu, S. Cui, J. Chen, M. Bao, Feature extraction of motor imagery eeg based on extreme learning machine auto encoder, in: Proceedings of ELM-2015, Vol. 1. – volume: 6 year: 2020 ident: b69 article-title: Alcoholic EEG signal classification with correlation dimension based distance metrics approach and modified adaboost classification publication-title: Heliyon – volume: 8 start-page: 92 year: 2016 ident: b9 article-title: The biomarkers for identifying preclinical Alzheimer’s disease via structural and functional magnetic resonance imaging publication-title: Front. Aging Neurosci. – volume: 23 start-page: 1149 year: 2010 end-page: 1157 ident: b38 article-title: Performance enhancement of extreme learning machine for multi-category sparse data classification problems publication-title: Eng. Appl. Artif. Intell. – volume: 7 start-page: 1060 year: 2017 ident: b79 article-title: Human emotion recognition with electroencephalographic multidimensional features by hybrid deep neural networks publication-title: Appl. Sci. – volume: 16 start-page: 599 year: 2019 ident: b56 article-title: A deep learning model for automated sleep stages classification using PSG signals publication-title: Int. J. Environ. Res. Public Health – volume: 2019 year: 2019 ident: b39 article-title: Globality-locality preserving maximum variance extreme learning machine publication-title: Complexity – year: 2020 ident: b21 article-title: Schizophrenia detection using multivariate empirical mode decomposition and entropy measures from multichannel EEG entropy measures from multichannel EEG signal publication-title: Biocybern. Biomed. Eng. – start-page: 305 year: 1990 end-page: 308 ident: b65 article-title: Kohonen’s self organizing feature maps for exploratory data analysis publication-title: Proceedings of the Proceedings of International Neural Networks Conference (INNC ’90) – start-page: 1 year: 2020 end-page: 11 ident: b23 article-title: Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals publication-title: Phys. Eng. Sci. Med. – volume: 2018 year: 2018 ident: b46 article-title: Sparse representation-based extreme learning machine for motor imagery EEG classification publication-title: Comput. Intell. Neurosci. – volume: 2015 year: 2015 ident: b32 article-title: Extreme learning machines on high dimensional and large data applications: A survey publication-title: Math. Probl. Eng. – volume: 48 start-page: 2725 year: 2015 end-page: 2737 ident: b51 article-title: Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI publication-title: Pattern Recognit. – volume: 2014 year: 2014 ident: b52 article-title: EEG-Based emotion recognition using deep learning network with principal component based covariate shift adaptation publication-title: Sci. World J. – volume: 15 start-page: 689 year: 2019 end-page: 698 ident: b55 article-title: Deep learning for Alzheimer’s disease classification using texture features publication-title: Curr. Med. Imaging Rev. – volume: 90 start-page: 220 year: 2019 end-page: 231 ident: b5 article-title: Weighted graph regularized sparse brain network construction for MCI identification publication-title: Pattern Recognit. – volume: 31 start-page: 729 year: 2021 end-page: 740 ident: b19 article-title: Detection and classification of electroencephalogram signals for epilepsy disease using machine learning methods publication-title: Int. J. Imaging Syst. Technol. – volume: 26 start-page: 1443 year: 2018 end-page: 1459 ident: b2 article-title: A high performance spelling system based on EEG-EOG signals with visual feedback publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 2008 year: 2008 ident: b81 article-title: Nonnegative matrix factorization with Gaussian process priors publication-title: Comput. Intell. Neurosci. – volume: 2014 year: 2014 ident: b30 article-title: Improved extreme learning machine and its application in image quality assessment publication-title: Math. Probl. Eng. – volume: 45 start-page: 2673 year: 1997 end-page: 2681 ident: b77 article-title: Bidirectional recurrent neural networks publication-title: IEEE Trans. Signal Process. – volume: 31 start-page: 3839 year: 2020 end-page: 3852 ident: b3 article-title: Subject-independent brain-computer interfaces based on deep convolutional neural networks publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 9 year: 2012 ident: b8 article-title: Sparse representation-based classification scheme for motor imagery-based brain-computer interface systems publication-title: J. Neural Eng. – volume: 20 start-page: 1112 year: 2011 end-page: 1125 ident: b67 article-title: Blind spectral unmixing based on sparse nonnegative matrix factorization publication-title: IEEE Trans. Image Process. – volume: 2020 year: 2020 ident: b36 article-title: Hybridized extreme learning machine model with salp swarm algorithm: A novel predictive model for hydrological application publication-title: Complexity – volume: 2014 year: 2014 ident: b70 article-title: Wavelets and morphological operators based classification of epilepsy risk levels publication-title: Math. Probl. Eng. – volume: 14 start-page: 808 year: 2020 ident: b10 article-title: An intelligent EEG classification methodology based on sparse representation enhanced deep learning networks publication-title: Front. Neurosci. – volume: 7 start-page: 5 year: 2020 ident: b13 article-title: A review of epileptic seizure detection using machine learning classifiers publication-title: Brain Inform. – volume: 54 start-page: 149 year: 2016 end-page: 161 ident: b48 article-title: Hierarchical multi-class SVM with ELM kernel for epileptic EEG signal classification publication-title: Med. Biol. Eng. Comput. – volume: 9 start-page: 1 year: 2019 end-page: 18 ident: b58 article-title: Classification of drowsiness levels based on a deep spatio-temporal convolutional bidirectional LSTM network using electroencephalography signals publication-title: Brain Sci. – volume: 28 start-page: 817 year: 2016 end-page: 833 ident: b71 article-title: Robotic grasping recognition using multi-modal deep extreme learning machine publication-title: Multidimens. Syst. Signal Process. – volume: 11 start-page: 2635 year: 2016 end-page: 2647 ident: b4 article-title: A high-security EEG-based login system with RSVP stimuli and dry electrodes publication-title: IEEE Trans. Inf. Forensics Secur. – volume: 37 start-page: 235 year: 2020 end-page: 244 ident: b26 article-title: Automatic detection of schizophrenia by applying deep learning over spectrogram images of EEG signals publication-title: Trait. Signal – reference: . – reference: G.B. Huang, Q.Y. Zhu, C.K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in: Proceedings of the IEEE International Joint Conference on Neural Networks, Vol. 2, 2004, pp. 985–990. – volume: 40 start-page: 324 year: 2020 end-page: 336 ident: b57 article-title: Classification of Pilots’ mental states using a multimodal deep learning network publication-title: Biocybern. Biomed. Eng. – volume: 2019 year: 2019 ident: b34 article-title: Application of extreme learning machine for predicting chlorophyll-a concentration inartificial upwelling processes publication-title: Math. Probl. Eng. – volume: 8 start-page: 39875 year: 2020 end-page: 39897 ident: b25 article-title: A framework for schizophrenia EEG signal classification with nature inspired optimization algorithms publication-title: IEEE Access – volume: 22 start-page: 1345 year: 2010 end-page: 1359 ident: b59 article-title: A survey on transfer learning publication-title: IEEE Trans. Knowl. Data Eng. – volume: 39 start-page: 574 year: 2012 end-page: 584 ident: b82 article-title: Feature subset selection wrapper based on mutual information and rough sets publication-title: Expert Syst. Appl. – volume: 23 start-page: 2101 year: 2013 end-page: 2106 ident: b74 article-title: Global minimization of multi-funnel functions using particle swarm optimization publication-title: Neural Comput. Appl. – start-page: 1 year: 2017 end-page: 14 ident: b6 article-title: Discriminative self-representation sparse regression for neuroimaging-based Alzheimer’s disease diagnosis publication-title: Brain Imaging Behav. – reference: A. Graves, Generating sequences with recurrent neural networks, 0000. – volume: 25 start-page: 2270 issue: 12 year: 2017 ident: 10.1016/j.asoc.2022.108416_b61 article-title: Seizure classification from EEG signals using transfer learning, semi-supervised learning and TSK fuzzy system publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2017.2748388 – volume: 12 year: 2017 ident: 10.1016/j.asoc.2022.108416_b85 article-title: Graph-based analysis of brain connectivity in schizophrenia publication-title: PLoS One doi: 10.1371/journal.pone.0188629 – volume: 37 start-page: 3284 year: 2010 ident: 10.1016/j.asoc.2022.108416_b89 article-title: Entropies based detection of epileptic seizures with artificial neural network classifiers publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2009.09.051 – volume: 22 start-page: 1345 issue: 10 year: 2010 ident: 10.1016/j.asoc.2022.108416_b59 article-title: A survey on transfer learning publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2009.191 – ident: 10.1016/j.asoc.2022.108416_b42 doi: 10.1016/j.procs.2016.07.422 – volume: 2019 year: 2019 ident: 10.1016/j.asoc.2022.108416_b39 article-title: Globality-locality preserving maximum variance extreme learning machine publication-title: Complexity doi: 10.1155/2019/1806314 – volume: 7 start-page: 401 year: 2012 ident: 10.1016/j.asoc.2022.108416_b90 article-title: Automatic diagnosis of epileptic EEG using entropies publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2011.07.007 – volume: 2015 year: 2015 ident: 10.1016/j.asoc.2022.108416_b49 article-title: Deep extreme learning machine and its application in EEG classification publication-title: Math. Probl. Eng. doi: 10.1155/2015/129021 – volume: 2020 start-page: 1 year: 2020 ident: 10.1016/j.asoc.2022.108416_b24 article-title: Schizophrenia EEG signal classification based on swarm intelligence computing publication-title: Comput. Intell. Neurosci. doi: 10.1155/2020/8853835 – volume: 11 start-page: 2635 issue: 12 year: 2016 ident: 10.1016/j.asoc.2022.108416_b4 article-title: A high-security EEG-based login system with RSVP stimuli and dry electrodes publication-title: IEEE Trans. Inf. Forensics Secur. doi: 10.1109/TIFS.2016.2577551 – volume: 34 start-page: 208 issue: 11 year: 2008 ident: 10.1016/j.asoc.2022.108416_b73 article-title: Ant colony algorithm parameters optimization publication-title: Comput. Eng. – volume: 136 start-page: 65 year: 2016 ident: 10.1016/j.asoc.2022.108416_b87 article-title: Automatic identification of epileptic seizures from EEG signals using linear programming boosting publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2016.08.013 – volume: 8 start-page: 1323 year: 2014 ident: 10.1016/j.asoc.2022.108416_b91 article-title: Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network publication-title: SiViP doi: 10.1007/s11760-012-0362-9 – volume: 235 start-page: 167 issue: 2 year: 2021 ident: 10.1016/j.asoc.2022.108416_b22 article-title: Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients publication-title: Proc. Inst. Mech. Eng. [H] doi: 10.1177/0954411920966937 – volume: 25 start-page: 549 year: 2014 ident: 10.1016/j.asoc.2022.108416_b29 article-title: Extreme learning machine publication-title: Neural Comput. Appl. doi: 10.1007/s00521-013-1522-8 – volume: 31 start-page: 729 issue: 2 year: 2021 ident: 10.1016/j.asoc.2022.108416_b19 article-title: Detection and classification of electroencephalogram signals for epilepsy disease using machine learning methods publication-title: Int. J. Imaging Syst. Technol. doi: 10.1002/ima.22486 – volume: 70 year: 2021 ident: 10.1016/j.asoc.2022.108416_b20 article-title: Automated accurate schizophrenia detection system using collatz pattern technique with EEG signals’ publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2021.102936 – volume: 16 start-page: 599 issue: 4 year: 2019 ident: 10.1016/j.asoc.2022.108416_b56 article-title: A deep learning model for automated sleep stages classification using PSG signals publication-title: Int. J. Environ. Res. Public Health doi: 10.3390/ijerph16040599 – volume: 23 start-page: 1149 issn: 0952-1976 issue: 7 year: 2010 ident: 10.1016/j.asoc.2022.108416_b38 article-title: Performance enhancement of extreme learning machine for multi-category sparse data classification problems publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2010.06.009 – volume: 7 start-page: 5 issue: 1 year: 2020 ident: 10.1016/j.asoc.2022.108416_b13 article-title: A review of epileptic seizure detection using machine learning classifiers publication-title: Brain Inform. doi: 10.1186/s40708-020-00105-1 – volume: 64 year: 2001 ident: 10.1016/j.asoc.2022.108416_b84 article-title: Indications of non linear deterministic and finite dimensional structures in time series of brain electrical activity: dependence on recording region and brain state publication-title: Phys. Rev. E doi: 10.1103/PhysRevE.64.061907 – volume: 28 start-page: 817 issue: 3 year: 2016 ident: 10.1016/j.asoc.2022.108416_b71 article-title: Robotic grasping recognition using multi-modal deep extreme learning machine publication-title: Multidimens. Syst. Signal Process. doi: 10.1007/s11045-016-0389-0 – start-page: 451 year: 2011 ident: 10.1016/j.asoc.2022.108416_b76 article-title: Glowworm swarm optimization for multimodal search spaces – volume: 40 start-page: 324 issue: 1 year: 2020 ident: 10.1016/j.asoc.2022.108416_b57 article-title: Classification of Pilots’ mental states using a multimodal deep learning network publication-title: Biocybern. Biomed. Eng. doi: 10.1016/j.bbe.2019.12.002 – ident: 10.1016/j.asoc.2022.108416_b78 – volume: 2014 year: 2014 ident: 10.1016/j.asoc.2022.108416_b30 article-title: Improved extreme learning machine and its application in image quality assessment publication-title: Math. Probl. Eng. doi: 10.1155/2014/426152 – volume: 39 start-page: 574 issue: 1 year: 2012 ident: 10.1016/j.asoc.2022.108416_b82 article-title: Feature subset selection wrapper based on mutual information and rough sets publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.07.048 – volume: 5 start-page: 1457 year: 2004 ident: 10.1016/j.asoc.2022.108416_b63 article-title: Non-negative matrix factorization with sparseness constraints publication-title: J. Mach. Learn. Res. – volume: 45 start-page: 2673 issue: 11 year: 1997 ident: 10.1016/j.asoc.2022.108416_b77 article-title: Bidirectional recurrent neural networks publication-title: IEEE Trans. Signal Process. doi: 10.1109/78.650093 – start-page: 895 year: 2006 ident: 10.1016/j.asoc.2022.108416_b11 article-title: Image denoising via learned dictionaries and sparse representation – volume: 9 start-page: 1 year: 2019 ident: 10.1016/j.asoc.2022.108416_b58 article-title: Classification of drowsiness levels based on a deep spatio-temporal convolutional bidirectional LSTM network using electroencephalography signals publication-title: Brain Sci. doi: 10.3390/brainsci9120348 – volume: 15 start-page: 689 issue: 7 year: 2019 ident: 10.1016/j.asoc.2022.108416_b55 article-title: Deep learning for Alzheimer’s disease classification using texture features publication-title: Curr. Med. Imaging Rev. doi: 10.2174/1573405615666190404163233 – volume: 11 start-page: 993 issue: 4 year: 2016 ident: 10.1016/j.asoc.2022.108416_b37 article-title: An automatic diagnosis system for hepatitis diseases based on genetic wavelet kernel extreme learning machine publication-title: J. Electr. Eng. Technol. doi: 10.5370/JEET.2016.11.4.993 – year: 2021 ident: 10.1016/j.asoc.2022.108416_b18 article-title: Epileptic EEG classification by using time-frequency images for deep learning publication-title: Int. J. Neural Syst. doi: 10.1142/S012906572150026X – volume: 7 start-page: 1060 year: 2017 ident: 10.1016/j.asoc.2022.108416_b79 article-title: Human emotion recognition with electroencephalographic multidimensional features by hybrid deep neural networks publication-title: Appl. Sci. doi: 10.3390/app7101060 – volume: 21 start-page: 123 issue: 2 year: 2011 ident: 10.1016/j.asoc.2022.108416_b1 article-title: Subject and class specific frequency bands selection for multi-class motor imagery classification publication-title: Int. J. Imaging Syst. Technol. doi: 10.1002/ima.20283 – start-page: 51 year: 2017 ident: 10.1016/j.asoc.2022.108416_b92 article-title: Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent elman neural network classifier – volume: 26 start-page: 1443 issue: 7 year: 2018 ident: 10.1016/j.asoc.2022.108416_b2 article-title: A high performance spelling system based on EEG-EOG signals with visual feedback publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2018.2839116 – volume: 48 start-page: 2725 issue: 8 year: 2015 ident: 10.1016/j.asoc.2022.108416_b51 article-title: Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2015.03.010 – volume: 8 start-page: 92 year: 2016 ident: 10.1016/j.asoc.2022.108416_b9 article-title: The biomarkers for identifying preclinical Alzheimer’s disease via structural and functional magnetic resonance imaging publication-title: Front. Aging Neurosci. doi: 10.3389/fnagi.2016.00092 – volume: 2015 year: 2015 ident: 10.1016/j.asoc.2022.108416_b35 article-title: A novel multiple instance learning method based on extreme learning machine publication-title: Comput. Intell. Neurosci. doi: 10.1155/2015/405890 – ident: 10.1016/j.asoc.2022.108416_b47 doi: 10.1007/978-3-319-28397-5_28 – volume: 107 start-page: 61 year: 2018 ident: 10.1016/j.asoc.2022.108416_b53 article-title: An automated system for epilepsy detection using EEG brain signals based on deep learning approach publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2018.04.021 – volume: 8 start-page: 134524 year: 2020 ident: 10.1016/j.asoc.2022.108416_b50 article-title: SessionNet: FEature similarity-based weighted ensemble learning for motor imagery classification publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3011140 – volume: 11 start-page: 288 issue: 3 year: 2007 ident: 10.1016/j.asoc.2022.108416_b88 article-title: Approximate entropy-based epileptic EEG detection using artificial neural networks publication-title: IEEE Trans. Inf. Technol. Biomed. doi: 10.1109/TITB.2006.884369 – volume: 16 start-page: 29 issue: 1 year: 2006 ident: 10.1016/j.asoc.2022.108416_b44 article-title: Classification of mental tasks from EEG signals using extreme learning machine publication-title: Int. J. Neural Syst. doi: 10.1142/S0129065706000482 – volume: 46 issue: 4 year: 2016 ident: 10.1016/j.asoc.2022.108416_b40 article-title: Fault detection in state variable filter circuit using kernel extreme learning machine (KELM) algorithm publication-title: Inform. MIDEM – start-page: 305 year: 1990 ident: 10.1016/j.asoc.2022.108416_b65 article-title: Kohonen’s self organizing feature maps for exploratory data analysis – start-page: 1 year: 2020 ident: 10.1016/j.asoc.2022.108416_b23 article-title: Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals publication-title: Phys. Eng. Sci. Med. – volume: 2019 year: 2019 ident: 10.1016/j.asoc.2022.108416_b34 article-title: Application of extreme learning machine for predicting chlorophyll-a concentration inartificial upwelling processes publication-title: Math. Probl. Eng. doi: 10.1155/2019/8719387 – volume: 61 start-page: 2341 issue: 9 year: 2013 ident: 10.1016/j.asoc.2022.108416_b64 article-title: Constrained overcomplete analysis operator learning for cosparse signal modelling publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2013.2250968 – start-page: 1 year: 2017 ident: 10.1016/j.asoc.2022.108416_b6 article-title: Discriminative self-representation sparse regression for neuroimaging-based Alzheimer’s disease diagnosis publication-title: Brain Imaging Behav. – volume: 59 start-page: 884 year: 2009 ident: 10.1016/j.asoc.2022.108416_b12 article-title: Multifocus image fusion and restoration with sparse representation publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2009.2026612 – year: 2020 ident: 10.1016/j.asoc.2022.108416_b14 – volume: 2008 year: 2008 ident: 10.1016/j.asoc.2022.108416_b81 article-title: Nonnegative matrix factorization with Gaussian process priors publication-title: Comput. Intell. Neurosci. doi: 10.1155/2008/361705 – volume: 2014 year: 2014 ident: 10.1016/j.asoc.2022.108416_b70 article-title: Wavelets and morphological operators based classification of epilepsy risk levels publication-title: Math. Probl. Eng. doi: 10.1155/2014/813197 – volume: 23 start-page: 2101 issue: 7–8 year: 2013 ident: 10.1016/j.asoc.2022.108416_b74 article-title: Global minimization of multi-funnel functions using particle swarm optimization publication-title: Neural Comput. Appl. doi: 10.1007/s00521-012-1158-0 – ident: 10.1016/j.asoc.2022.108416_b28 doi: 10.1109/IJCNN.2004.1380068 – start-page: 9 year: 2015 ident: 10.1016/j.asoc.2022.108416_b43 article-title: Analyze EEG signals with extreme learning machine based on PMIS feature selection publication-title: Int. J. Mach. Learn. Cybern. – volume: 46 issue: 1 year: 2016 ident: 10.1016/j.asoc.2022.108416_b41 article-title: Robust extreme learning machine with its application to indoor positioning publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2015.2399420 – volume: 2014 year: 2014 ident: 10.1016/j.asoc.2022.108416_b52 article-title: EEG-Based emotion recognition using deep learning network with principal component based covariate shift adaptation publication-title: Sci. World J. doi: 10.1155/2014/627892 – start-page: 115 year: 1991 ident: 10.1016/j.asoc.2022.108416_b72 article-title: Preventing premature convergence in genetic algorithms by preventing incest – volume: 20 year: 2021 ident: 10.1016/j.asoc.2022.108416_b15 article-title: Minireview of epilepsy detection techniques based on electroencephalogram signals publication-title: Front. Syst. Neurosci. – volume: 57 start-page: 147 year: 2019 ident: 10.1016/j.asoc.2022.108416_b45 article-title: A hierarchical semi-supervised extreme learning machine method for EEG recognition publication-title: Med. Biol. Eng. Comput. doi: 10.1007/s11517-018-1875-3 – volume: 5 start-page: 1 issue: 1 year: 2009 ident: 10.1016/j.asoc.2022.108416_b68 article-title: Performance comparison of particle swarm optimization with traditional clustering algorithms used in self organizing map publication-title: Int. J. Comput. Intell. – volume: 80 start-page: 17849 year: 2021 ident: 10.1016/j.asoc.2022.108416_b17 article-title: Machine learning-based EEG signals classification model for epileptic seizure detection publication-title: Multimedia Tools Appl. doi: 10.1007/s11042-021-10597-6 – volume: 214 start-page: 108 issue: 1 year: 2009 ident: 10.1016/j.asoc.2022.108416_b75 article-title: A comparative study of artificial bee colony algorithm publication-title: Appl. Math. Comput. – volume: 14 start-page: 808 year: 2020 ident: 10.1016/j.asoc.2022.108416_b10 article-title: An intelligent EEG classification methodology based on sparse representation enhanced deep learning networks publication-title: Front. Neurosci. doi: 10.3389/fnins.2020.00808 – volume: 9 year: 2012 ident: 10.1016/j.asoc.2022.108416_b8 article-title: Sparse representation-based classification scheme for motor imagery-based brain-computer interface systems publication-title: J. Neural Eng. doi: 10.1088/1741-2560/9/5/056002 – volume: 20 start-page: 1112 issue: 4 year: 2011 ident: 10.1016/j.asoc.2022.108416_b67 article-title: Blind spectral unmixing based on sparse nonnegative matrix factorization publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2010.2081678 – volume: 2020 year: 2020 ident: 10.1016/j.asoc.2022.108416_b36 article-title: Hybridized extreme learning machine model with salp swarm algorithm: A novel predictive model for hydrological application publication-title: Complexity doi: 10.1155/2020/8206245 – volume: 39 start-page: 202 issue: 1 year: 2012 ident: 10.1016/j.asoc.2022.108416_b86 article-title: Detection of epileptic electroencephalogram based on perumutation entropy and support vector machine publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.07.008 – volume: 54 start-page: 149 issue: 1 year: 2016 ident: 10.1016/j.asoc.2022.108416_b48 article-title: Hierarchical multi-class SVM with ELM kernel for epileptic EEG signal classification publication-title: Med. Biol. Eng. Comput. doi: 10.1007/s11517-015-1351-2 – volume: 106 start-page: 71 year: 2019 ident: 10.1016/j.asoc.2022.108416_b80 article-title: Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2019.01.013 – volume: 31 start-page: 3839 issue: 10 year: 2020 ident: 10.1016/j.asoc.2022.108416_b3 article-title: Subject-independent brain-computer interfaces based on deep convolutional neural networks publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2019.2946869 – volume: 83 start-page: 103 year: 2018 ident: 10.1016/j.asoc.2022.108416_b54 article-title: Automated depression analysis using convolutional neural networks from speech publication-title: J. Biomed. Inform. doi: 10.1016/j.jbi.2018.05.007 – volume: 6 issue: 12 year: 2020 ident: 10.1016/j.asoc.2022.108416_b69 article-title: Alcoholic EEG signal classification with correlation dimension based distance metrics approach and modified adaboost classification publication-title: Heliyon doi: 10.1016/j.heliyon.2020.e05689 – year: 2020 ident: 10.1016/j.asoc.2022.108416_b21 article-title: Schizophrenia detection using multivariate empirical mode decomposition and entropy measures from multichannel EEG entropy measures from multichannel EEG signal publication-title: Biocybern. Biomed. Eng. doi: 10.1016/j.bbe.2020.05.008 – ident: 10.1016/j.asoc.2022.108416_b27 doi: 10.24251/HICSS.2020.393 – volume: 90 start-page: 220 year: 2019 ident: 10.1016/j.asoc.2022.108416_b5 article-title: Weighted graph regularized sparse brain network construction for MCI identification publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2019.01.015 – volume: 37 start-page: 235 issue: 2 year: 2020 ident: 10.1016/j.asoc.2022.108416_b26 article-title: Automatic detection of schizophrenia by applying deep learning over spectrogram images of EEG signals publication-title: Trait. Signal doi: 10.18280/ts.370209 – volume: 8 start-page: 39875 year: 2020 ident: 10.1016/j.asoc.2022.108416_b25 article-title: A framework for schizophrenia EEG signal classification with nature inspired optimization algorithms publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2975848 – volume: 2015 year: 2015 ident: 10.1016/j.asoc.2022.108416_b32 article-title: Extreme learning machines on high dimensional and large data applications: A survey publication-title: Math. Probl. Eng. doi: 10.1155/2015/103796 – volume: 23 year: 2021 ident: 10.1016/j.asoc.2022.108416_b16 article-title: Classification of EEG signals for epileptic seizures detection and eye states identification using Jacobi polynomial transforms-based measures of complexity and least-squares support vector machines publication-title: Inform. Med. Unlocked – volume: 22 start-page: 1078 issue: 10 year: 2000 ident: 10.1016/j.asoc.2022.108416_b66 article-title: ICA Mixture models for unsupervised classification of non-Gaussian classes and automatic context switching in blind signal separation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.879789 – volume: 9 start-page: 811 year: 2020 ident: 10.1016/j.asoc.2022.108416_b33 article-title: An advanced pruning method in the architecture of extreme learning machines using L1 regularization and bootstrapping publication-title: Electronics doi: 10.3390/electronics9050811 – volume: 2020 year: 2020 ident: 10.1016/j.asoc.2022.108416_b31 article-title: A hybrid method based on extreme learning machine and self organizing map for pattern classification publication-title: Comput. Intell. Neurosci. doi: 10.1155/2020/2918276 – volume: 2018 year: 2018 ident: 10.1016/j.asoc.2022.108416_b46 article-title: Sparse representation-based extreme learning machine for motor imagery EEG classification publication-title: Comput. Intell. Neurosci. doi: 10.1155/2018/9593682 – volume: 2021 year: 2021 ident: 10.1016/j.asoc.2022.108416_b83 article-title: An affinity propagation-based clustering method for the temporal dynamics management of high-speed railway passenger demand publication-title: J. Adv. Transp. – ident: 10.1016/j.asoc.2022.108416_b7 doi: 10.1109/CBMS.2012.6266371 – volume: 127 start-page: 323 year: 2019 ident: 10.1016/j.asoc.2022.108416_b93 article-title: A novel approach for classification of epileptic seizures using matrix determinant publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.03.021 – start-page: 1354 year: 2020 ident: 10.1016/j.asoc.2022.108416_b60 article-title: Decoding movement imagination and execution from eeg signals using bci-transfer learning method based on relation network – volume: 124 start-page: 202 year: 2020 ident: 10.1016/j.asoc.2022.108416_b62 article-title: EEG Based multi-class seizure type classification using convolutional neural network and transfer learning publication-title: Neural Netw. doi: 10.1016/j.neunet.2020.01.017 |
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