Neural Processing Letters

, Volume 50, Issue 2, pp 1281–1301 | Cite as

Classification of EEG Signals Using Hybrid Feature Extraction and Ensemble Extreme Learning Machine

  • Weijie Ren
  • Min HanEmail author


Electroencephalogram (EEG) signals play an important role in clinical diagnosis and cognitive neuroscience. Automatic classification of EEG signals is gradually becoming the research focus, which contains two procedures: feature extraction and classification. In the phase of feature extraction, a hybrid feature extraction method is proposed and the features are derived by performing linear and nonlinear feature extraction methods, which can describe abundant properties of original EEG signals. In order to eliminate irrelevant and redundant features, feature selection based on class separability is employed to select the optimal feature subset. In the phase of classification, this paper presents a novel ensemble extreme learning machine based on linear discriminant analysis. Linear discriminant analysis is used to transform training subsets that are generated by bootstrap method, through which we can increase the differences of basic classifiers and reduce generalization errors of ensemble extreme learning machine. Experiments on two different EEG datasets are conducted in this study. Class separability is investigated to verify the effectiveness of feature extraction methods. The overall classification results show that compared with other similar studies, the proposed method can significantly enhance the performance of EEG signals classification.


EEG signals Classification Feature extraction Ensemble extreme learning machine 



This work was supported by the National Natural Science Foundation of China (61773087 and 61374154).


  1. 1.
    Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B (2007) A review of classification algorithms for EEG-based brain–computer interfaces. J Neural Eng 4(2):R1CrossRefGoogle Scholar
  2. 2.
    Mo H, Zhao Y (2016) Motor imagery electroencephalograph classification based on optimized support vector machine by magnetic bacteria optimization algorithm. Neural Process Lett 44(1):185–197CrossRefGoogle Scholar
  3. 3.
    Roebuck A, Monasterio V, Gederi E et al (2013) A review of signals used in sleep analysis. Physiol Meas 35(1):R1CrossRefGoogle Scholar
  4. 4.
    Song Y, Zhang J (2013) Automatic recognition of epileptic EEG patterns via extreme learning machine and multiresolution feature extraction. Expert Syst Appl 40(14):5477–5489CrossRefGoogle Scholar
  5. 5.
    Wang D, Ren D, Li K, Feng Y, Ma D, Yan X, Wang G (2018) Epileptic seizure detection in long-term EEG recordings by using wavelet-based directed transfer function. IEEE Trans Biomed Eng. CrossRefGoogle Scholar
  6. 6.
    Gallego-Jutglà E, Solé-Casals J, Vialatte FB, Elgendi M, Cichocki A, Dauwels J (2015) A hybrid feature selection approach for the early diagnosis of Alzheimer’s disease. J Neural Eng 12(1):016018CrossRefGoogle Scholar
  7. 7.
    Goldstein MR, Peterson MJ, Sanguinetti JL, Tononi G, Ferrarelli F (2015) Topographic deficits in alpha-range resting EEG activity and steady state visual evoked responses in schizophrenia. Schizophr Res 168(1):145–152CrossRefGoogle Scholar
  8. 8.
    Acharya UR, Sree SV, Swapna G, Martis RJ, Suri JS (2013) Automated EEG analysis of epilepsy: a review. Knowl Based Syst 45:147–165CrossRefGoogle Scholar
  9. 9.
    Wu W, Nagarajan S, Chen Z (2016) Bayesian machine learning: EEG/MEG signal processing measurements. IEEE Signal Process Mag 33(1):14–36CrossRefGoogle Scholar
  10. 10.
    Balli T, Palaniappan R (2010) Classification of biological signals using linear and nonlinear features. Physiol Meas 31(7):903–920CrossRefGoogle Scholar
  11. 11.
    Wei Q, Wang Y, Gao X, Gao S (2007) Amplitude and phase coupling measures for feature extraction in an EEG-based brain–computer interface. J Neural Eng 4(2):120CrossRefGoogle Scholar
  12. 12.
    Al-Fahoum AS, Al-Fraihat AA (2014) Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN Neurosci 2014:730218CrossRefGoogle Scholar
  13. 13.
    Polat K, Güneş S (2007) Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl Math Comput 187(2):1017–1026MathSciNetzbMATHGoogle Scholar
  14. 14.
    Chua KC, Chandran V, Acharya UR, Lim CM (2011) Application of higher order spectra to identify epileptic EEG. J Med Syst 35(6):1563–1571CrossRefGoogle Scholar
  15. 15.
    Goldfine AM, Victor JD, Conte MM, Bardin JC, Schiff ND (2011) Determination of awareness in patients with severe brain injury using EEG power spectral analysis. Clin Neurophysiol 122(11):2157–2168CrossRefGoogle Scholar
  16. 16.
    Bhattacharyya A, Sharma M, Pachori RB, Sircar P, Acharya UR (2018) A novel approach for automated detection of focal EEG signals using empirical wavelet transform. Neural Comput Appl 29(8):47–57CrossRefGoogle Scholar
  17. 17.
    Zhang Y, Liu B, Ji X, Huang D (2017) Classification of EEG signals based on autoregressive model and wavelet packet decomposition. Neural Process Lett 45(2):365–378CrossRefGoogle Scholar
  18. 18.
    Güler NF, Übeyli ED, Güler İ (2005) Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst Appl 29(3):506–514CrossRefGoogle Scholar
  19. 19.
    Shayegh F, Sadri S, Amirfattahi R, Ansari-Asl K (2014) A model-based method for computation of correlation dimension, Lyapunov exponents and synchronization from depth-EEG signals. Comput Methods Progr Biomed 113(1):323–337CrossRefzbMATHGoogle Scholar
  20. 20.
    Bai Y, Liang Z, Li X, Voss LJ, Sleigh JW (2015) Permutation Lempel-Ziv complexity measure of electroencephalogram in GABAergic anaesthetics. Physiol Meas 36(12):2483–2501CrossRefGoogle Scholar
  21. 21.
    Song Y, Liò P (2010) A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine. J Biomed Sci Eng 3(6):556–567CrossRefGoogle Scholar
  22. 22.
    Ocak H (2009) Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 36(2):2027–2036CrossRefGoogle Scholar
  23. 23.
    Kannathal N, Choo ML, Acharya UR, Sadasivan PK (2005) Entropies for detection of epilepsy in EEG. Comput Methods Progr Biomed 80(3):187–194CrossRefGoogle Scholar
  24. 24.
    Acharya UR, Fujita H, Sudarshan VK, Bhat S, Koh JE (2015) Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowl Based Syst 88:85–96CrossRefGoogle Scholar
  25. 25.
    Subha DP, Joseph PK, Acharya UR, Lim CM (2010) EEG signal analysis: a survey. J Med Syst 34(2):195–212CrossRefGoogle Scholar
  26. 26.
    Yoon H, Yang K, Shahabi C (2005) Feature subset selection and feature ranking for multivariate time series. IEEE Trans Knowl Data Eng 17(9):1186–1198CrossRefGoogle Scholar
  27. 27.
    Wu W, Chen Z, Gao X, Li Y, Brown EN, Gao S (2015) Probabilistic common spatial patterns for multichannel EEG analysis. IEEE Trans Pattern Anal Mach Intell 37(3):639–653CrossRefGoogle Scholar
  28. 28.
    Qi F, Li Y, Wu W (2015) RSTFC: a novel algorithm for spatio-temporal filtering and classification of single-trial EEG. IEEE Trans Neural Netw Learn Syst 26(12):3070–3082CrossRefMathSciNetGoogle Scholar
  29. 29.
    Long J, Li Y, Yu T, Gu Z (2012) Target selection with hybrid feature for BCI-based 2-D cursor control. IEEE Trans Biomed Eng 59(1):132–140CrossRefGoogle Scholar
  30. 30.
    Smart O, Burrell L (2015) Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data. Eng Appl Artif Intell 39:198–214CrossRefGoogle Scholar
  31. 31.
    Löfhede J, Thordstein M, Löfgren N, Flisberg A, Rosa-Zurera M, Kjellmer I, Lindecrantz K (2010) Automatic classification of background EEG activity in healthy and sick neonates. J Neural Eng 7(1):016007CrossRefGoogle Scholar
  32. 32.
    Sun ZR, Cai YX, Wang SJ, Wang CD, Zheng YQ, Chen YH, Chen YC (2018) Multi-view intact space learning for tinnitus classification in resting state EEG. Neural Process Lett. CrossRefGoogle Scholar
  33. 33.
    Hu HW, Chen YL, Tang K (2013) A novel decision-tree method for structured continuous-label classification. IEEE Trans Cybern 43(6):1734–1746CrossRefGoogle Scholar
  34. 34.
    Thomas EM, Temko A, Lightbody G, Marnane WP, Boylan GB (2010) Gaussian mixture models for classification of neonatal seizures using EEG. Physiol Meas 31(7):1047–1064CrossRefGoogle Scholar
  35. 35.
    Zhang GP (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 30(4):451–462CrossRefMathSciNetGoogle Scholar
  36. 36.
    Angulo C, Ruiz FJ, González L, Ortega JA (2006) Multi-classification by using tri-class SVM. Neural Process Lett 23(1):89–101CrossRefGoogle Scholar
  37. 37.
    Subasi A, Gursoy MI (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37(12):8659–8666CrossRefGoogle Scholar
  38. 38.
    Termenon M, Graña M, Barrós-Loscertales A, Ávila C (2013) Extreme learning machines for feature selection and classification of cocaine dependent patients on structural MRI data. Neural Process Lett 38(3):375–387CrossRefGoogle Scholar
  39. 39.
    Yuan Q, Zhou W, Li S, Cai D (2011) Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res 96(1):29–38CrossRefGoogle Scholar
  40. 40.
    Liu N, Wang H (2010) Ensemble based extreme learning machine. IEEE Signal Process Lett 17(8):754–757CrossRefGoogle Scholar
  41. 41.
    Samat A, Du P, Liu S, Li J, Cheng L (2014) E2LMs: ensemble extreme learning machines for hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 7(4):1060–1069CrossRefGoogle Scholar
  42. 42.
    Krogh A, Vedelsby J (1995) Neural network ensembles, cross validation and active learning. In: Tesauro G, Touretzky D, Leen T (eds) Advances in neural information processing systems, vol 7. MIT Press, Cambridge, MA, pp 231–238Google Scholar
  43. 43.
    Sun S, Zhang C, Zhang D (2007) An experimental evaluation of ensemble methods for EEG signal classification. Pattern Recognit Lett 28(15):2157–2163CrossRefGoogle Scholar
  44. 44.
    Übeyli ED (2008) Wavelet/mixture of experts network structure for EEG signals classification. Expert Syst Appl 34(3):1954–1962CrossRefGoogle Scholar
  45. 45.
    Zhang Y, Dong Z, Wang S, Ji G, Yang J (2015) Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with Tsallis entropy and generalized eigenvalue proximal support vector machine (GEPSVM). Entropy 17(4):1795–1813CrossRefGoogle Scholar
  46. 46.
    Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circul Physiol 278(6):H2039–H2049CrossRefGoogle Scholar
  47. 47.
    Han M, Liu X (2013) Feature selection techniques with class separability for multivariate time series. Neurocomputing 110:29–34CrossRefGoogle Scholar
  48. 48.
    Niu P, Ma Y, Li M, Yan S, Li G (2016) A kind of parameters self-adjusting extreme learning machine. Neural Process Lett 44(3):813–830CrossRefGoogle Scholar
  49. 49.
    Huang GB (2014) An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput 6(3):376–390CrossRefMathSciNetGoogle Scholar
  50. 50.
    Yao C, Lu Z, Li J, Xu Y, Han J (2014) A subset method for improving linear discriminant analysis. Neurocomputing 138:310–315CrossRefGoogle Scholar
  51. 51.
    Blake CL, Merz CJ (1998) UCI Repository of machine learning databases. Accessed 1 Nov 2014
  52. 52.
    Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys Rev E 64(6):061907CrossRefGoogle Scholar
  53. 53.
    Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A (2010) OP-ELM: optimally pruned extreme learning machine. IEEE Trans Neural Netw 21(1):158–162CrossRefGoogle Scholar
  54. 54.
    Skurichina M, Duin RPW (2002) Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal Appl 5(2):121–135CrossRefMathSciNetzbMATHGoogle Scholar
  55. 55.
    Orhan U, Hekim M, Ozer M (2011) EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Syst Appl 38(10):13475–13481CrossRefGoogle Scholar
  56. 56.
    Acharya UR, Molinari F, Sree SV, Chattopadhyay S, Ng KH, Suri JS (2012) Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control 7(4):401–408CrossRefGoogle Scholar
  57. 57.
    Acharya UR, Sree SV, Alvin APC, Suri JS (2012) Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework. Expert Syst Appl 39(10):9072–9078CrossRefGoogle Scholar
  58. 58.
    Peker M, Sen B, Delen D (2016) A novel method for automated diagnosis of epilepsy using complex-valued classifiers. IEEE J Biomed Health Inform 20(1):108–118CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina

Personalised recommendations