A Novel Feature Fusion Approach for Classification of Motor Imagery EEG Based on Hierarchical Extreme Learning Machine

Lopes DSF. EEG and MEG: relevance to neuroscience. Neuron. 2013;80(5):1112.

Article  MathSciNet  Google Scholar 

Ruffino C, Papaxanthis C, Lebon F. Neural plasticity during motor learning with motor imagery practice: review and perspectives. Neuroscience. 2017;341:61.

Article  Google Scholar 

Wong CK, Luo Q, Zotev V, et al. Automatic cardiac cycle determination directly from EEG-fMRI data by multi-scale peak detection method. J Neurosci Methods. 2018.

Jain A, Abbas B, Farooq O, et al. Fatigue detection and estimation using auto-regression analysis in EEG. International Conference on Advances in Computing, Communications and Informatics. IEEE. 2016:1092-1095.

Ahirwal MK, Kumar A, Singh GK. Adaptive filtering of EEG/ERP through bounded range artificial bee colony (BR-ABC) algorithm. Digital Signal Process. 2014;25(1):164–72.

Article  Google Scholar 

Ahirwal MK, Kumar A, Singh GK. Adaptive filtering of EEG/ERP through noise cancellers using an improved PSO algorithm. Swarm Evol Comput. 2014;14:76–91.

Article  Google Scholar 

Pan X, Xue L, Lu Y, et al. Hybrid particle swarm optimization with simulated annealing. Multimed Tools Appl. 2019;78(21):29921–36.

Article  Google Scholar 

Taran S, Bajaj V. Sleep apnea detection using artificial bee colony optimize Hermite basis functions for EEG signals. IEEE Trans Instrum Meas. 2019.

Rajaguru H, Prabhakar S K. Power spectral density with correlation dimension for epilepsy classification from EEG signal. International Conference on Communication and Electronics Systems. 2017:376-379.

Al-Marridi AZ, Mohamed A, Erbad A. Convolutional autoencoder approach for EEG compression and reconstruction in m-health systems. In Proc. 14th Int. Wireless Commun. Mobile Comput. Conf. (IWCMC), Jun. 2018, pp. 370-375.

Tang X, Yang J, Wan H. A hybrid SAE and CNN classifier for motor imagery EEG classification // Artificial Intelligence and Algorithms in Intelligent Systems. 2019.

George ST, Subathra MSP, Sairamya NJ, et al. Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform. Biocybern Biomed Eng. 2020.

Tang Z. Conditional adversarial domain adaptation neural network for motor imagery EEG decoding. Entropy. 2020;22(1):96.

Article  MathSciNet  Google Scholar 

Mammone N, Ieracitano C, Morabito FC. A deep CNN approach to decode motor preparation of upper limbs from time-frequency maps of EEG signals at source level. Neural Netw. 2020;124:357–72.

Article  Google Scholar 

Tortora S, Ghidoni S, Chisari C, et al. Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network. J Neural Eng. 2020.

Cai H, Sha X, Han X, Wei S, Hu B. Pervasive EEG diagnosis of depression using deep belief network with three-electrodes EEG collector. In Proc. IEEE Int. Conf. Bioinformatics Biomed. (BIBM), Dec. 2016, pp, 1239-1246.

Cheng L, Li D, Yu G, et al. A motor imagery EEG feature extraction method based on energy principal component analysis and deep belief networks. IEEE Access. 2020;8:21453–72.

Article  Google Scholar 

Dong W, Woźniak M, Wu J, et al. Denoising aggregation of graph neural networks by using principal component analysis. IEEE Trans Industr Inf. 2022;19(3):2385–94.

Article  Google Scholar 

Huang S, Zhang J, Yang C, et al. The interval grey QFD method for new product development: integrate with LDA topic model to analyze online reviews. Eng Appl Artif Intell. 2022;114.

Li Z, Nie F, Wu D, et al. Sparse trace ratio LDA for supervised feature selection. IEEE Trans Cybern. 2023.

Han X, Su J, Hong Y, et al. Mid-to long-term electric load forecasting based on the EMD-Isomap-Adaboost Model. Sustainability. 2022;14(13):7608.

Article  Google Scholar 

Yang B, Xiang M, Zhang Y. Multi-manifold discriminant Isomap for visualization and classification. Pattern Recogn. 2016;55:215–30.

Article  MATH  Google Scholar 

Liu C, Jaja J, Pessoa L. LEICA: Laplacian eigenmaps for group ICA decomposition of fMRI data. Neuroimage. 2017;169:363–73.

Article  Google Scholar 

Ward JL, Lumsden SL. Locally linear embedding: dimension reduction of massive protostellar spectra. Mon Not R Astron Soc. 2016;461(2).

Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(Nov):2579-2605.

Lee JA, Peluffo-Ordoñez DH, Verleysen M. Multiscale stochastic neighbor embedding: towards parameter-free dimensionality reduction. ESANN. 2014.

Richhariya B, Tanveer M. EEG signal classification using universum support vector machine. Expert Syst Appl. 2018.

Kasun LLC, Zhou H, Huang G, et al. Representational learning with ELMs for big data. Intelligent Systems IEEE. 2013;28(6):31–4.

Google Scholar 

Huang GB, Zhu Q, Siew C. Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of the 2004 International Joint Conference on Neural Networks; 2004. vol. 2, pp. 985-990.

Tang J, Deng C, Huang GB. Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst. 2016;27(4):809–21.

Article  MathSciNet  Google Scholar 

Zhu WT, Miao J, Qing LY (2014) Constrained extreme learning machine: a novel highly discriminative random feedforward neural network, 2014 International Joint Conference on Neural Networks (IJCNN2014). Beijing, July 6-11, 2014. United Stated, IEEE.

Duan L, Bao M, Cui S, et al. Motor imagery EEG classification based on kernel hierarchical extreme learning machine. Cogn Comput. 2017;9(6):1–8.

Article  Google Scholar 

Birbaumer N. Data sets Ia for the BCI competition II. http://www.bbci.de/competition//ii/#datasets.

Mensh BD, Werfel J, Seung HS. BCI competition 2003-data set Ia: combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals. IEEE Trans Biomed Eng. 2004;51(6):1052–6.

Article  Google Scholar 

Sun S, Zhang C. Assessing features for electroencephalographic signal categorization. IEEE International Conference on Acoustics, Speech, and Signal Processing. 2005. Proceedings. IEEE, 2005:v/417-v/420 Vol. 5.

Wang B, Jun L, Bai J, et al. EEG recognition based on multiple types of information by using wavelet packet transform and neural networks. Engineering in Medicine and Biology Society, 2005. IEEE-Embs 2005. International Conference of the. IEEE. 2005:5377-5380.

Wu T, Yan GZ, Yang BH, et al. EEG feature extraction based on wavelet packet decomposition for brain computer interface. Measurement. 2008;41(6):618–25.

Article  Google Scholar 

Kayikcioglu T, Aydemir O. A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data. Elsevier Science Inc. 2010.

Abdel-Basset M, Manogaran G, El-Shahat D, et al. A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem. Futur Gener Comput Syst. 2018;85:129–45.

Article  Google Scholar 

留言 (0)

沒有登入
gif