Classification of EEG Signals Based on Image Representation of Statistical Features

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1043)


This work presents an image classification approach to EEG brainwave classification. The proposed method is based on the representation of temporal and statistical features as a 2D image, which is then classified using a deep Convolutional Neural Network. A three-class mental state problem is investigated, in which subjects experience either relaxation, concentration, or neutral states. Using publicly available EEG data from a Muse Electroencephalography headband, a large number of features describing the wave are extracted, and subsequently reduced to 256 based on the Information Gain measure. These 256 features are then normalised and reshaped into a \(16\times 16\) grid, which can be expressed as a grayscale image. A deep Convolutional Neural Network is then trained on this data in order to classify the mental state of subjects. The proposed method obtained an out-of-sample classification accuracy of 89.38%, which is competitive with the 87.16% of the current best method from a previous work.


Machine learning Convolutional neural networks Image recognition Mental state classification Electroencephalography 


  1. 1.
    Caton, R.: The electric currents of the brain. Am. J. EEG Technol. 10(1), 12–14 (1970)CrossRefGoogle Scholar
  2. 2.
    Llinás, R.R.: Intrinsic electrical properties of mammalian neurons and cns function: a historical perspective. Front. Cell. Neurosci. 8, 320 (2014)CrossRefGoogle Scholar
  3. 3.
    Bird, J.J., Manso, L.J., Ribiero, E.P., Ekart, A., Faria, D.R.: A study on mental state classification using EEG-based brain-machine interface. In: 9th International Conference on Intelligent Systems, IEEE (2018)Google Scholar
  4. 4.
    Bird, J.J., Ekart, A., Buckingham, C.D., Faria, D.R.: Mental emotional sentiment classification with an EEG-based brain-machine interface. In: The International Conference on Digital Image and Signal Processing (DISP 2019). Springer, (2019)Google Scholar
  5. 5.
    Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: Deap: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)CrossRefGoogle Scholar
  6. 6.
    Tripathi, S., Acharya, S., Sharma, R.D., Mittal, S., Bhattacharya, S.: Using deep and convolutional neural networks for accurate emotion classification on deap dataset. In: Twenty-Ninth IAAI Conference (2017)Google Scholar
  7. 7.
    Purves, D., Augustine, G., Fitzpatrick, D., Hall, W., LaMantia, A., McNamara, J., Williams, S.: Neuroscience. Sinauer Associates, Sunderland (2004)Google Scholar
  8. 8.
    Britton, J.W., Frey, L.C., Hopp, J., Korb, P., Koubeissi, M., Lievens, W., Pestana-Knight, E., St, E.L.: Electroencephalography (EEG): An introductory text and atlas of normal and abnormal findings in adults, children, and infants. American Epilepsy Society, Chicago (2016)Google Scholar
  9. 9.
    Buzsáki, G., Anastassiou, C.A., Koch, C.: The origin of extracellular fields and currents–EEG, ECOG, LFP and spikes. Nat. Rev. Neurosci. 13(6), 407 (2012)CrossRefGoogle Scholar
  10. 10.
    Cohen, M.X.: Analyzing Neural Time Series Data: Theory and Practice. MIT press, Cambridge (2014)CrossRefGoogle Scholar
  11. 11.
    Picard, R.W.: Affective Computing. MIT press, Cambridge (2000)Google Scholar
  12. 12.
    Pantic, M., Rothkrantz, L.J.: Toward an affect-sensitive multimodal human-computer interaction. Proc. IEEE 91(9), 1370–1390 (2003)CrossRefGoogle Scholar
  13. 13.
    Rouast, P.V., Adam, M., Chiong, R.: Deep learning for human affect recognition: insights and new developments. In: IEEE Transactions on Affective Computing (2019)Google Scholar
  14. 14.
    Abujelala, M., Abellanoza, C., Sharma, A., Makedon, F.: Brain-EE: Brain enjoyment evaluation using commercial EEG headband. In: Proceedings of the 9th ACM International Conference on Pervasive Technologies Related to Assistive Environments, p. 33. ACM (2016)Google Scholar
  15. 15.
    Abhang, P.A., Gawali, B.W.: Correlation of EEG images and speech signals for emotion analysis. Br. J. Appl. Sci. Technol. 10(5), 1–13 (2015)CrossRefGoogle Scholar
  16. 16.
    Gevins, A., Smith, M.E., McEvoy, L., Yu, D.: High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cerebral cortex (New York, NY: 1991), vol. 7, no. 4, pp. 374–385 (1997)Google Scholar
  17. 17.
    Zhang, X., Wu, D.: On the vulnerability of cnn classifiers in EEG-based BCIS. IEEE Trans. Neural Syst. Rehabil. Eng. 27(5), 814–825 (2019)CrossRefGoogle Scholar
  18. 18.
    Wang, X., Magno, M., Cavigelli, L., Mahmud, M., Cecchetto, C., Vassanelli, S., Benini, L.: Embedded classification of local field potentials recorded from rat barrel cortex with implanted multi-electrode array. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–4. IEEE (2018)Google Scholar
  19. 19.
    Wang, X., Magno, M., Cavigelli, L., Mahmud, M., Cecchetto, C., Vassanelli, S., Benini, L.: Rat cortical layers classification extracting evoked local field potential images with implanted multi-electrode sensor. In: 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 1–6, IEEE (2018)Google Scholar
  20. 20.
    Mahmud, M., Kaiser, M.S., Hussain, A., Vassanelli, S.: Applications of deep learning and reinforcement learning to biological data. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2063–2079 (2018)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Tan, P.-N.: Introduction to Data Mining. Pearson Education India, Chennai (2018)Google Scholar
  22. 22.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.orgzbMATHGoogle Scholar
  23. 23.
    Zwillinger, D., Kokoska, S.: CRC Standard Probability and Statistics Tables and Formulae. Chapman & Hall, London (2000)zbMATHGoogle Scholar
  24. 24.
    Montgomery, D.C., Runger, G.C.: Applied Statistics and Probability for Engineers. John Wiley & Sons, New Jersey (2010)zbMATHGoogle Scholar
  25. 25.
    Strang, G.: Linear Algebra and its Applications. Brooks Cole, California (2006)zbMATHGoogle Scholar
  26. 26.
    Chiu, T.Y., Leonard, T., Tsui, K.-W.: The matrix-logarithmic covariance model. J. Am. Stat. Assoc. 91(433), 198–210 (1996)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Van Loan, C.: Computational frameworks for the fast Fourier transform, vol. 10, Siam (1992)Google Scholar
  28. 28.
    LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., Jackel, L.D.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems, pp. 396–404 (1990)Google Scholar
  29. 29.
    Oppenheim, A.V., Willsky, A.S., Nawab, S.: Signals and Systems. Prentice Hall, New Jersey (1996)Google Scholar
  30. 30.
    Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649 (2012)Google Scholar
  31. 31.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  33. 33.
    Chollet, F., et al.: Keras. (2015)
  34. 34.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv e-prints, p. arXiv:1412.6980, Dec 2014Google Scholar
  35. 35.
    Bird, J.J., Faria, D.R., Manso, L.J., Ekart, A., Buckingham, C.D.: A deep evolutionary approach to bioinspired classifier optimisation for brain-machine interaction. Complexity 2019, 14 (2019)CrossRefGoogle Scholar
  36. 36.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, vol. 2, IJCAI 1995, pp. 1137–1143 (1995)Google Scholar
  37. 37.
    López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Stützle, T., Birattari, M.: The irace package: iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43–58 (2016)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Burke, E.K., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Qu, R.: Hyper-heuristics: a survey of the state of the art. J. Oper. Res. Soc. 64(12), 1695–1724 (2013)CrossRefGoogle Scholar
  39. 39.
    Martín, A., Lara-Cabrera, R., Fuentes-Hurtado, F., Naranjo, V., Camacho, D.: Evodeep: a new evolutionary approach for automatic deep neural networks parametrisation. J. Parallel Distrib. Comput. 117, 180–191 (2018)CrossRefGoogle Scholar
  40. 40.
    Assunçao, F., Lourenço, N., Machado, P., Ribeiro, B.: Denser: deep evolutionary network structured representation. arXiv preprint arXiv:1801.01563 (2018)
  41. 41.
    Bird, J.J., Ekart, A., Faria, D.R.: Evolutionary optimisation of fully connected artificial neural network topology. In: SAI Computing Conference 2019, SAI (2019)Google Scholar
  42. 42.
    Montgomery, D.C.: Design and Analysis of Experiments, 8th edn. John Wiley & Sons, New Jersey (2012)Google Scholar
  43. 43.
    Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Engineering and Applied ScienceAston UniversityBirminghamUK

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