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A Literature Review on Data Conversion Methods on EEG for Convolution Neural Network Applications

  • Chi Qin Lai
  • Haidi IbrahimEmail author
  • Mohd Zaid Abdullah
  • Jafri Malin Abdullah
  • Shahrel Azmin Suandi
  • Azlinda Azman
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 547)

Abstract

Convolution neural network (CNN) presents high robustness in computer vision applications. In state-of-the-art methods, CNN is being used in EEG processing for various classification and problem solving. To enable EEG to fit in the CNN architecture, data conversion of EEG has to be done. The ways of data conversion need to be investigated in order to fully utilize the information. From the study, it was found that the simplest way of re-arranging the signal is by creating a two dimensional matrix of channels versus time points. There are approaches that compute Pearson correlation coefficients and fit them into a two dimensional matrix to represent the input signal. There are also methods which extract frequency components and fit them in matrix structure as channels versus frequency components, such as power spectral density. Other approaches includes graph representation and wavelet components.

Keywords

CNN PCC Data conversion EEG 

Notes

Acknowledgements

This research is partly supported by the Ministry of Higher Education (MoHE), Malaysia, via Trans-disciplinary Research Grant Scheme (TRGS) with grant number 203/PELECT/6768002.

References

  1. 1.
    da Silva, F.L.: EEG and MEG: relevance to neuroscience. Neuron 80(5), 1112–1128 (2013)CrossRefGoogle Scholar
  2. 2.
    Fisher, J.A.N., Huang, S., Ye, M., Nabili, M., Wilent, W.B., Krauthamer, V., Myers, M.R., Welle, C.G.: Real-time detection and monitoring of acute brain injury utilizing evoked electroencephalographic potentials. IEEE Trans. Neural Syst. Rehabil. Eng. 24(9), 1003–1012 (2016)CrossRefGoogle Scholar
  3. 3.
    Schmeiser, B., Zentner, J., Steinhoff, B., Brandt, A., Schulze-Bonhage, A., Kogias, E., Hammen, T.: The role of presurgical EEG parameters and of reoperation for seizure outcome in temporal lobe epilepsy. Seizure 51(Supplement C), 174–179 (2017)Google Scholar
  4. 4.
    Tylova, L., Kukal, J., Vysata, O.: Predictive models in diagonosis of alzheimer’s disease from EEG. Acta Polytech. 52(2), 94–97 (2013)Google Scholar
  5. 5.
    Lopetegui, E., Zapirain, B.G., Mendez, A.: Tennis computer game with brain control using EEG signals. In: 2011 16th International Conference on Computer Games (CGAMES), pp. 228–234. July 2011Google Scholar
  6. 6.
    Norman, S.L., Dennison, M., Wolbrecht, E., Cramer, S.C., Srinivasan, R., Reinkensmeyer, D.J.: Movement anticipation and EEG: implications for bci-contingent robot therapy. IEEE Trans. Neural Syst. Rehabil. Eng. 24(8), 911–919 (2016)CrossRefGoogle Scholar
  7. 7.
    Sereshkeh, A.R., Trott, R., Bricout, A., Chau, T.: EEG classification of covert speech using regularized neural networks. IEEE/ACM Trans. Audio Speech Lang. Process. 25(12), 2292–2300 (2017)CrossRefGoogle Scholar
  8. 8.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001)Google Scholar
  9. 9.
    Wolfshaar, J.V.D., Karaaba, M.F., Wiering, M.A.: Deep convolutional neural networks and support vector machines for gender recognition. In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 188–195. Dec 2015Google Scholar
  10. 10.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, vol. 1, pp. 1097–1105. ser. NIPS’12. USA: Curran Associates Inc., (2012)Google Scholar
  11. 11.
    Priddy, K.L., Keller, P.E.: Artificial neural networks: an introduction (SPIE Tutorial Texts in Optical Engineering, Vol. TT68). SPIE- International Society for Optical Engineering (2005)Google Scholar
  12. 12.
    Ma, L., Minett, J.W., Blu, T., Wang, W.S.Y.: Resting state EEG-based biometrics for individual identification using convolutional neural networks. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2848–2851. Aug 2015Google Scholar
  13. 13.
    Cecotti, H., Graser, A.: Convolutional neural networks for P300 detection with application to brain-computer interfaces. IEEE Trans. Pattern Anal. Mach. Intel. 33(3), 433–445 (2011)CrossRefGoogle Scholar
  14. 14.
    Sakhavi, S., Guan, C., Yan, S.: Learning temporal information for brain-computer interface using convolutional neural networks. IEEE Trans. Neural Netw. Learn. Syst. 1–11 (2018)Google Scholar
  15. 15.
    Wen, Z., Xu, R., Du, J.: A novel convolutional neural networks for emotion recognition based on EEG signal. In: 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), pp. 672–677. Dec 2017Google Scholar
  16. 16.
    Mei, H., Xu, X.: EEG-based emotion classification using convolutional neural network. In: 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), pp. 130–135. Dec 2017Google Scholar
  17. 17.
    Cheng, C., Wei, X., Jian, Z.: Emotion recognition algorithm based on convolution neural network. In: 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 1–5. Nov 2017Google Scholar
  18. 18.
    Song, T., Zheng, W., Song, P., Cui, Z.: EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans. Affect. Comput. 1–1 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Chi Qin Lai
    • 1
  • Haidi Ibrahim
    • 1
    Email author
  • Mohd Zaid Abdullah
    • 2
  • Jafri Malin Abdullah
    • 3
  • Shahrel Azmin Suandi
    • 1
  • Azlinda Azman
    • 4
  1. 1.School of Electrical and Electronic Engineering, Engineering CampusUniversiti Sains MalaysiaNibong TebalMalaysia
  2. 2.Collaborative Microelectronic Design Excellence Centre (CEDEC)Bayan LepasMalaysia
  3. 3.Department of Neurosciences, School of Medical SciencesUniversiti Sains MalaysiaKubang KerianMalaysia
  4. 4.School of Social SciencesUniversiti Sains MalaysiaPulau PinangMalaysia

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