Wireless Personal Communications

, Volume 108, Issue 2, pp 659–681 | Cite as

Individual and Mutual Feature Processed ELM Model for EEG Signal Based Brain Activity Classification

  • Kapil JunejaEmail author
  • Chhavi Rana


BCI deals to map the brain signal or activity to evaluate the human behaviour, activities or disease. The aim of this research is to utilize the different features of EEG signal to recognize the brain activity. The composite feature model with ELM classification method is presented in this research to recognize the human activity. In this paper, multiple aspects including time domain, frequency domain and least square evaluation based features are processed under ELM classifier to recognize the human-activities. Multiple quantified features are generated under each time, frequency and the least square categories. These features are processed individually and mutually with probabilistic evaluation to expand the processing-featureset. This expanded-composite featureset is trained under ELM (Extreme Learning Machine) classifier to perform intra-class and inter-class classification. The experimentation is applied on five distinctive experiments of Dataset IIIa of BCI completion III. Each experiment is conducted with variant training and testing instances. The evaluation results identified that the proposed hybrid model has achieved the average accuracy over 80%. Comparative results are generated against ANN, SVM, KNN and Multiscale Wavelet Kernel ELM by utilizing each kind of individual and mutual feature. The results taken from various experimentations have validated that the proposed model has improved the accuracy against each of the existing feature processed classification methods.


EEG Brain activity classification BCI Thought processing 



  1. 1.
    Arunkumar, N., Ramkumar, K., Venkatraman, V., Abdulhay, E., Fernandes, S. L., Kadry, S., et al. (2017). Classification of focal and non focal EEG using entropies. Pattern Recognition Letters, 94, 112–117.CrossRefGoogle Scholar
  2. 2.
    Tang, Z., Li, C., & Sun, S. (2017). Single-trial EEG classification of motor imagery using deep convolutional neural networks. Optik - International Journal for Light and Electron Optics, 130, 11–18.CrossRefGoogle Scholar
  3. 3.
    Wenting, T., & Sun, S. (2012). A subject transfer framework for EEG classification. Neurocomputing, 82, 109–116.CrossRefGoogle Scholar
  4. 4.
    Samiee, K., Kiranyaz, S., Gabbouj, M., & Saramäki, T. (2015). Long-term epileptic EEG classification via 2D mapping and textural features. Expert Systems with Applications, 42(20), 7175–7185.CrossRefGoogle Scholar
  5. 5.
    Cuesta-Frau, D., Pau, M.-M., Nunez, J. J., Sandra, O.-C., & Pico, A. M. (2017). Noisy EEG signals classification based on entropy metrics. Performance assessment using fi rst and second generation statistics. Computers in Biology and Medicine, 87, 141–151.CrossRefGoogle Scholar
  6. 6.
    Kocadagli, O., & Langari, R. (2017). Classification of EEG signals for epileptic seizures using hybrid artificial neural networks based wavelet transforms and fuzzy relations. Expert Systems with Applications, 88, 419–434.CrossRefGoogle Scholar
  7. 7.
    Satapathy, S. K., Dehuri, S., & Jagadev, A. K. (2017). ABC optimized RBF network for classification of EEG signal for epileptic seizure identification. Egyptian Informatics Journal, 18(1), 55–66.CrossRefGoogle Scholar
  8. 8.
    Sunil Kumar, T., Kanhangad, T. S., & Pachori, R. B. (2015). Classification of seizure and seizure-free EEG signals using local binary patterns. Biomedical Signal Processing and Control, 15, 33–40.CrossRefGoogle Scholar
  9. 9.
    Martín-Smith, P., Ortega, J., Asensio-Cubero, J., Gan, J. Q., & Ortiz, A. (2017). A supervised filter method for multi-objective feature selection in EEG classification based on multi-resolution analysis for BCI. Neurocomputing, 250, 45–56.CrossRefGoogle Scholar
  10. 10.
    Sturm, I., Lapuschkin, S., Samek, W., & Müller, K.-R. (2016). Interpretable deep neural networks for single-trial EEG classification. Journal of Neuroscience Methods, 274, 141–145.CrossRefGoogle Scholar
  11. 11.
    Aliakbaryhosseinabadi, S., Kamavuako, E. N., Jiang, N., Farina, D., & Mrachacz-Kersting, N. (2017). Classification of EEG signals to identify variations in attention during motor task execution. Journal of Neuroscience Methods, 284, 27–34.CrossRefGoogle Scholar
  12. 12.
    Hari Krishna, D., Pasha, I. A., & Satya Savithri, T. (2016). Classification of EEG motor imagery multi class signals based on cross correlation. Procedia Computer Science, 85, 490–495.CrossRefGoogle Scholar
  13. 13.
    Mirvaziri, H., & Mobarakeh, Z. S. (2017). Improvement of EEG-based motor imagery classification using ring topology-based particle swarm optimization. Biomedical Signal Processing and Control, 32, 69–75.CrossRefGoogle Scholar
  14. 14.
    Ma, Z., Tan, Z.-H., & Guo, J. (2016). Feature selection for neutral vector in EEG signal classification. Neurocomputing, 174, 937–945.CrossRefGoogle Scholar
  15. 15.
    Jaiswal, A. K., & Banka, H. (2017). Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals. Biomedical Signal Processing and Control, 34, 81–92.CrossRefGoogle Scholar
  16. 16.
    Satapathy, S. K., Dehuri, S., & Jagadev, A. K. (2017). EEG signal classification using PSO trained RBF neural network for epilepsy identification. Informatics in Medicine Unlocked, 6, 1–11.CrossRefGoogle Scholar
  17. 17.
    Bhati, D., Sharma, M., Pachori, R. B., & Gadre, V. M. (2017). Time–frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification. Digital Signal Processing, 62, 259–273.CrossRefGoogle Scholar
  18. 18.
    Ameri, R., Pouyan, A., & Abolghasemi, V. (2016). Projective dictionary pair learning for EEG signal classification in brain computer interface applications. Neurocomputing, 218, 382–389.CrossRefGoogle Scholar
  19. 19.
    Siuly, Y. L. (2014). A novel statistical algorithm for multiclass EEG signal classification. Engineering Applications of Artificial Intelligence, 34, 154–167.CrossRefGoogle Scholar
  20. 20.
    Lahiri, R., Rakshit, P., & Konar, A. (2017). Evolutionary perspective for optimal selection of EEG electrodes and features. Biomedical Signal Processing and Control, 36, 113–137.CrossRefGoogle Scholar
  21. 21.
    Kang, H., & Choi, S. (2014). Bayesian common spatial patterns for multi-subject EEG classification. Neural Networks, 57, 39–50.CrossRefzbMATHGoogle Scholar
  22. 22.
    Uehara, T., Sartori, M., Tanaka, T., & Fiori, S. (2017). Robust averaging of covariances for EEG recordings classification in motor imagery brain–computer interfaces. Neural Computation, 29(6), 1631–1666.MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Zhang, Y., Zhou, G., Jin, J., Zhao, Q., Wang, X., & Cichocki, A. (2016). Sparse Bayesian classification of EEG for brain–computer interface. IEEE Transactions on Neural Networks and Learning Systems, 27(11), 2256–2267.MathSciNetCrossRefGoogle Scholar
  24. 24.
    He, L., Hu, D., Wan, M., Wen, Y., von Deneen, K. M., & Zhou, M. (2016). Common Bayesian network for classification of EEG-based multiclass motor imagery BCI. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(6), 843–854.CrossRefGoogle Scholar
  25. 25.
    Qi, F., Li, Y., & Wu, W. (2015). RSTFC: A novel algorithm for spatio-temporal filtering and classification of single-trial EEG. IEEE Transactions on Neural Networks and Learning Systems, 26(12), 3070–3082.MathSciNetCrossRefGoogle Scholar
  26. 26.
    Peng, Y., & Bao-Liang, L. (2016). Discriminative manifold extreme learning machine and applications to image and EEG signal classification. Neurocomputing, 174, 265–277.CrossRefGoogle Scholar
  27. 27.
    Tang, Q., Wang, J., & Wang, H. (2014). L1-norm based discriminative spatial pattern for single-trial EEG classification. Biomedical Signal Processing and Control, 10, 313–321.CrossRefGoogle Scholar
  28. 28.
    Alcn, O. F., Siuly, S., Bajaj, V., Guo, Y., Sengur, A., & Zhang, Y. (2016). Multi-category EEG signal classification developing time-frequency texture features based Fisher Vector encoding method. Neurocomputing, 218, 251–258.CrossRefGoogle Scholar
  29. 29.
    Yin, Z., & Zhang, J. (2017). Cross-session classification of mental workload levels using EEG and an adaptive deep learning model. Biomedical Signal Processing and Control, 33, 30–47.CrossRefGoogle Scholar
  30. 30.
    Atyabi, A., Shic, F., & Naples, A. (2016). Mixture of autoregressive modeling orders and its implication on single trial EEG classification. Expert Systems with Applications, 65, 164–180.CrossRefGoogle Scholar
  31. 31.
    Soman, S., & Jayadeva. (2015). High performance EEG signal classification using classifiability and the twin SVM. Applied Soft Computing, 30, 305–318.CrossRefGoogle Scholar
  32. 32.
    Salazar-Varas, R., & Vazquez, R. A. (2018). Evaluating spiking neural models in the classification of motor imagery EEG signals using short calibration sessions. Applied Soft Computing, 67, 232–244.CrossRefGoogle Scholar
  33. 33.
    Hettiarachchi, I. T., Babaei, T., Nguyen, T., Lim, C. P., & Nahavandi, S. (2018). A fresh look at functional link neural network for motor imagery-based brain–computer interface. Journal of Neuroscience Methods, 305, 28–35.CrossRefGoogle Scholar
  34. 34.
    Zhang, Y., Wang, Y., Zhou, G., Jin, J., Wang, B., Wang, X., et al. (2018). Multi-kernel extreme learning machine for EEG classification in brain–computer interfaces. Expert Systems with Applications, 96, 302–310.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, University Institute of Engineering and Technology, Maharshi Dayanand UniversityRohtakIndia
  2. 2.Maharshi Dayanand UniversityRohtakIndia

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