The Classification of EEG Signal Using Different Machine Learning Techniques for BCI Application

  • Mamunur RashidEmail author
  • Norizam Sulaiman
  • Mahfuzah Mustafa
  • Sabira Khatun
  • Bifta Sama Bari
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1015)


Brain-Computer Interface (BCI) or Human-Machine Interface now becoming vital biomedical engineering and technology field which applying EEG technologies to provide assistive device technology (AT) to humans. Hence, this paper presents the results of analyzing EEG signals from various human cognitive states to extract the suitable EEG features that can be employed to control BCI devices which can be used by disabled or paralyzed people. The EEG features in term of power spectral density, spectral centroids, standard deviation and entropy are selected and investigated from two different mental exercises; (i) quick solving math and (ii) relax (do nothing). The selected features then are classified using Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K-Nearest Neighbors (k-NN) classifier. Among all these features, the best accuracy have been achieved by the power spectral density. The accuracies of this feature are 95%, 100%, 100% with LDA, SVM and K-NN respectively. Finally, the translation algorithm will be constructed using selected and classified EEG features to control the BCI devices.



The author would like to acknowledge the great supports by his postgraduate supervisor, research team members, Faculty of Electrical & Electronics Engineering as well as Universiti Malaysia Pahang for providing financial support through research grant, RDU180396.


  1. 1.
    van de Laar, B., et al.: Experiencing BCI control in a popular computer game. IEEE Trans. Comput. Intell. AI Games 5(2), 176–184 (2013)CrossRefGoogle Scholar
  2. 2.
    Jiang, D., Yin, J.: Research of auxiliary game platform based on BCI technology. In: Asia-Pacific Conference on Information Processing, APCIP 2009, pp. 424–428 (2009)Google Scholar
  3. 3.
    Vo, K., Nguyen, D.N., Kha, H.H., Dutkiewicz, E.: Real-time analysis on ensemble SVM scores to reduce P300-Speller intensification time. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, pp. 4383–4386 (2017)Google Scholar
  4. 4.
    Aydemir, O., Kayikcioglu, T.: Decision tree structure based classification of EEG signals recorded during two dimensional cursor movement imagery. J. Neurosci. Methods 229, 68–75 (2014). ISSN 0165-0270CrossRefGoogle Scholar
  5. 5.
    Zhang, B., Jiang, H., Dong, L.: Classification of EEG signal by WT-CNN model in emotion recognition system. In: 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC), Oxford, pp. 109–114 (2017)Google Scholar
  6. 6.
    Latif, M.Y., et al.: Brain computer interface based robotic arm control. In: 2017 International Smart Cities Conference (ISC2), Wuxi, pp. 1–5 (2017)Google Scholar
  7. 7.
    Singla, R., Khosla, A., Jha, R.: Influence of stimuli colour in SSVEP-based BCI wheelchair control using support vector machines. J. Med. Eng. Technol. 38(3), 125–134 (2014)CrossRefGoogle Scholar
  8. 8.
    Anindya, S.F., Rachmat, H.H., Sutjiredjeki, E.: A prototype of SSVEP-based BCI for home appliances control. In: 2016 1st International Conference on Biomedical Engineering (IBIOMED), Yogyakarta, pp. 1–6 (2016)Google Scholar
  9. 9.
    Kumar, P., Saini, R., Sahu, P.K., Roy, P.P., Dogra, D.P., Balasubramanian, R.: Neuro-phone: an assistive framework to operate smartphone using EEG signals. In: 2017 IEEE Region 10 Symposium (TENSYMP), Cochin, pp. 1–5 (2017)Google Scholar
  10. 10.
    Chakladar, D.D., Chakraborty, S.: EEG based emotion classification using “correlation based subset selection”. Biol. Inspired Cogn. Arch. 24, 98–106 (2018). ISSN 2212-683XGoogle Scholar
  11. 11.
    Anh, V.H., Van, M.N., Ha, B.B., Quyet, T.H.: A real-time model based support vector machine for emotion recognition through EEG. In: 2012 International Conference on Control, Automation and Information Sciences (ICCAIS), Ho Chi Minh City, pp. 191–196 (2012)Google Scholar
  12. 12.
    Liu, Y.-J., Yu, M., Zhao, G., Song, J., Ge, Y., Shi, Y.: Real-time movie-induced discrete emotion recognition from EEG Signals. IEEE Trans. Affect. Comput. 1 (2017). Scholar
  13. 13.
    Pan, J., Li, Y., Wang, J.: An EEG-based brain-computer interface for emotion recognition. In: 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, pp. 2063–2067 (2016)Google Scholar
  14. 14.
    Djamal, E.C., Lodaya, P.: EEG based emotion monitoring using wavelet and learning vector quantization. In: 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Yogyakarta, pp. 1–6 (2017)Google Scholar
  15. 15.
    Murugappan, M.: Human emotion classification using wavelet transform and KNN. In: 2011 International Conference on Pattern Analysis and Intelligence Robotics, Putrajaya, pp. 148–153 (2011)Google Scholar
  16. 16.
    Kaur, B., Singh, D., Roy, P.P.: EEG based emotion classification mechanism in BCI. Procedia Comput. Sci. 132, 752–758 (2018). ISSN 1877-0509CrossRefGoogle Scholar
  17. 17.
    Ortiz-Rosario, A., Adeli, H.: Brain-computer interface technologies: from signal to action. Rev. Neurosci. 24(5), 537–552 (2013)CrossRefGoogle Scholar
  18. 18.
    Knott, V., Mahoney, C., Kennedy, S., Evans, K.: EEG power, frequency, asymmetry and coherence in male depression. Psychiatry Res.: Neuroimaging 106(2), 123–140 (2001)CrossRefGoogle Scholar
  19. 19.
    Chaouachi, M., Jraidi, I., Frasson, C.: Modeling mental workload using EEG features for intelligent systems. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 50–61. Springer, Heidelberg (2011). The cognitive activation theory of stress. Psychoneuroendocrinology 29, 567–592 (2004)CrossRefGoogle Scholar
  20. 20.
    Sulaiman, N., Taib, M.N., Lias, S., Murat, Z.H., Aris, S.A.M., Hamid, N.H.A.: Novel methods for stress features identification using EEG signals. Int. J. Simul. Syst. Sci. Technol. 12(1), 27–33 (2011)Google Scholar
  21. 21.
    Shen, K.Q., Ong, C.J., Li, X.P., Hui, Z., Wilder-Smith, E.P.V.: A feature selection method for multilevel mental fatigue EEG classification. IEEE Trans. Biomed. Eng. 54(7), 1231–1237 (2007)CrossRefGoogle Scholar
  22. 22.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain–computer interfaces. J. Neural Eng. 4, 24 (2007). <inria-00134950>CrossRefGoogle Scholar
  23. 23.
  24. 24.
    Atkinson, J., Campos, D.: Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Expert Syst. Appl. 47, 35–41 (2016)CrossRefGoogle Scholar
  25. 25.
    Otsuka, T., et al.: Effects of mandibular deviation on brain activation during clenching: an fMRI preliminary study. Cranio 27, 88–93 (2009)CrossRefGoogle Scholar
  26. 26.
    Aydın, S., Saraoğlu, H.M., Kara, S.: Log energy entropy-based EEG classification with multilayer neural networks in seizure. Ann. Biomed. Eng. 37(12), 2626–2630 (2009)CrossRefGoogle Scholar
  27. 27.
    Cui, G., Zhao, Q., Cao, J., Cichocki, A.: Hybrid-BCI: classification of auditory and visual related potentials. In: 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS), Kitakyushu, pp. 297–300 (2014)Google Scholar
  28. 28.
    Hortal, E., Iáñez, E., Úbeda, A., Planelles, D., Costa, Á., Azorín, J.M.: Selection of the best mental tasks for a SVM-based BCI system. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), San Diego, CA, pp. 1483–1488 (2014)Google Scholar
  29. 29.
    Jian, H.L., Tang, K.T.: Improving classification accuracy of SSVEP based BCI using RBF SVM with signal quality evaluation. In: 2014 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Kuching, pp. 302–306 (2014)Google Scholar
  30. 30.
    Bose, R., Khasnobish, A., Bhaduri, S., Tibarewala, D.N.: Performance analysis of left and right lower limb movement classification from EEG. In: 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, pp. 174–179 (2016)Google Scholar
  31. 31.
    Chanel, G., Kierkels, J.J., Soleymani, M., Pun, T.: Short-term emotion assessment in a recall paradigm. Int. J. Hum.-Comput. Stud. 67, 607–627 (2009)CrossRefGoogle Scholar
  32. 32.
    Koelstra, S., et al.: Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds.) BI 2010. LNCS (LNAI), vol. 6334, pp. 89–100. Springer, Heidelberg (2010). Scholar
  33. 33.
    Murugappan, M., Nagarajan, R., Yaacob, S.: Combining spatial filtering and wavelet transform for classifing human emotions using EEG Signals. J. Med. Biol. Eng. 31, 45–51 (2011)CrossRefGoogle Scholar
  34. 34.
    Bastos-Filho, T.F., Ferreira, A., Atencio, A.E., Arjunan, S., Kumar, D.: Evaluation of feature extraction techniques in emotional state recognition. In: 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), pp. 1–6 (2012)Google Scholar
  35. 35.
    Jatupaiboon, N., Pan-ngum, S., Israsena, P.: Emotion classification using minimal EEG channels and frequency bands. In: 2013 10th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 21–24 (2013)Google Scholar
  36. 36.
    Lokannavar, S., Lahane, P., Gangurde, A., Chidre, P.: Emotion recognition using EEG signals. Emotion 4, 54–56 (2015)Google Scholar
  37. 37.
    Srinivas, V.: Wavelet based emotion recognition using RBF algorithm (2016).

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Mamunur Rashid
    • 1
    Email author
  • Norizam Sulaiman
    • 1
  • Mahfuzah Mustafa
    • 1
  • Sabira Khatun
    • 1
  • Bifta Sama Bari
    • 1
  1. 1.Faculty of Electrical and Electronics EngineeringUniversiti Malaysia PahangPekanMalaysia

Personalised recommendations