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Feature Extraction for a Genetic Programming-Based Brain-Computer Interface

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Book cover Intelligent Systems (BRACIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13653))

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Abstract

Brain-Computer Interfaces (BCI) open a two-way communication channel between a computer and the brain: while the brain can control the computer, the computer can induce changes in the brain through feedback. This mechanism is used in post-stroke motor rehabilitation, in which a BCI provides feedback by classifying signals collected from a patient’s brain. Single Feature Genetic Programming (SFGP) can create classifiers for these signals. However, the Genetic Programming (GP) step in SFGP requires a set of extracted features to generate its model. To the best of our knowledge, the LogPower function is the only initial feature extraction function used in SFGP. Nevertheless, other functions can improve the quality of the generated classifiers. Thus, we analyze new initial feature extraction functions for GP in SFGP. We test the Common Spatial Patterns, Nonlinear Energy, Average Power Spectral Density, and Curve Length methods on two datasets suitable for post-stroke rehabilitation training. The results obtained show that the analyzed functions outperform LogPower in all our experiments, with a kappa value up to 25.20% better. We further test the proposed methods on a third dataset, created with low-cost equipment. In this case, we show that the Average Power Spectral Density function outperforms LogPower by 11.39% when three electrodes are used. Thus, we demonstrate that the new approach can be used with low-cost equipment and a small number of electrodes, reducing the financial costs of treatment and improving patients’ comfort.

Supported by CNPq, Capes, FAPEMIG, and UFJF.

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Notes

  1. 1.

    Approved by the ethics committee of Federal University of Juiz de Fora under the number 47866121.5.0000.5147.

  2. 2.

    https://openbci.com/.

  3. 3.

    https://sites.google.com/view/bci-comp-wcci.

  4. 4.

    https://github.com/DEAP/deap.

  5. 5.

    https://github.com/ghdesouza/bci.

References

  1. Abdallah, N., Khawandi, S., Daya, B., Chauvet, P.: A survey of methods for the construction of a brain computer interface. In: 2017 Sensors Networks Smart and Emerging Technologies (SENSET), pp. 1–4. IEEE (2017)

    Google Scholar 

  2. Alamdari, N., Haider, A., Arefin, R., Verma, A.K., Tavakolian, K., Fazel-Rezai, R.: A review of methods and applications of brain computer interface systems. In: 2016 IEEE International Conference on Electro Information Technology (EIT), pp. 0345–0350. IEEE (2016)

    Google Scholar 

  3. Ang, K.K., Chin, Z.Y., Wang, C., Guan, C., Zhang, H.: Filter bank common spatial pattern algorithm on BCI competition iv datasets 2a and 2b. Front. Neurosci. 6, 39 (2012)

    Article  Google Scholar 

  4. Ang, K.K., Guan, C.: Brain-computer interface in stroke rehabilitation. J. Comput. Sci. Eng. 7(2), 139–146 (2013)

    Article  Google Scholar 

  5. Bashashati, A., Fatourechi, M., Ward, R.K., Birch, G.E.: A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J. Neural Eng. 4(2), R32 (2007)

    Article  Google Scholar 

  6. Boonyakitanont, P., Lek-Uthai, A., Chomtho, K., Songsiri, J.: A review of feature extraction and performance evaluation in epileptic seizure detection using EEG. Biomed. Signal Process. Control 57, 101702 (2020)

    Article  Google Scholar 

  7. Broyden, C.G.: The convergence of a class of double-rank minimization algorithms 1. general considerations. IMA J. Appl. Math. 6(1), 76–90 (1970)

    Google Scholar 

  8. Eslahi, S.V., Dabanloo, N.J., Maghooli, K.: A GA-based feature selection of the EEG signals by classification evaluation: application in BCI systems. arXiv preprint arXiv:1903.02081 (2019)

  9. Fadzal, C., Mansor, W., Khuan, L.: Review of brain computer interface application in diagnosing dyslexia. In: 2011 IEEE Control and System Graduate Research Colloquium, pp. 124–128. IEEE (2011)

    Google Scholar 

  10. Feigin, V.L., et al.: Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet Neurol. 20(10), 795–820 (2021)

    Article  Google Scholar 

  11. Frey, J.: Comparison of an open-hardware electroencephalography amplifier with medical grade device in brain-computer interface applications. arXiv preprint arXiv:1606.02438 (2016)

  12. Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, San Diego (1990)

    MATH  Google Scholar 

  13. Gramfort, A., et al.: MEG and EEG data analysis with MNE-python. Front. Neurosci. 7, 267 (2013)

    Article  Google Scholar 

  14. Gupta, A., et al.: On the utility of power spectral techniques with feature selection techniques for effective mental task classification in noninvasive BCI. IEEE Trans. Syst. Man Cybern. Syst. 51(5), 3080–3092 (2019)

    Article  Google Scholar 

  15. Jayaram, V., Barachant, A.: MOABB: trustworthy algorithm benchmarking for BCIs. J. Neural Eng. 15(6), 066011 (2018)

    Article  Google Scholar 

  16. Jeong, J.H., Lee, D.H., Ahn, H.J., Lee, S.W.: Towards brain-computer interfaces for drone swarm control. In: 2020 8th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–4. IEEE (2020)

    Google Scholar 

  17. Kaiser, J.F.: On a simple algorithm to calculate the ‘energy’ of a signal. In: International Conference on Acoustics, Speech, and Signal Processing, pp. 381–384. IEEE (1990)

    Google Scholar 

  18. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  19. Leeb, R., Lee, F., Keinrath, C., Scherer, R., Bischof, H., Pfurtscheller, G.: Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Trans. Neural Syst. Rehabil. Eng. 15(4), 473–482 (2007)

    Article  Google Scholar 

  20. Leeb, R., Lee, F., Keinrath, C., Scherer, R., Bischof, H., Pfurtscheller, G.: Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Trans. Neural Syst. Rehabil. Eng. 15(4), 473–482 (2007)

    Article  Google Scholar 

  21. Li, Z., et al.: Enhancing BCI-based emotion recognition using an improved particle swarm optimization for feature selection. Sensors 20(11), 3028 (2020)

    Article  Google Scholar 

  22. Miao, M., Zhang, W., Hu, W., Wang, R.: An adaptive multi-domain feature joint optimization framework based on composite kernels and ant colony optimization for motor imagery EEG classification. Biomed. Signal Process. Control 61, 101994 (2020)

    Article  Google Scholar 

  23. Nakanishi, I., Ozaki, K., Li, S.: Evaluation of the brain wave as biometrics in a simulated driving environment. In: 2012 BIOSIG-Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG), pp. 1–5. IEEE (2012)

    Google Scholar 

  24. Pawar, D., Dhage, S.: Feature extraction methods for electroencephalography based brain-computer interface: a review. IAENG Int. J. Comput. Sci. 47(3) (2020)

    Google Scholar 

  25. Qian, X., et al.: Brain-computer-interface-based intervention re-normalizes brain functional network topology in children with attention deficit/hyperactivity disorder. Transl. Psychiatry 8(1), 1–11 (2018)

    Article  Google Scholar 

  26. Ravi, A., Beni, N.H., Manuel, J., Jiang, N.: Comparing user-dependent and user-independent training of CNN for SSVEP BCI. J. Neural Eng. 17(2), 026028 (2020)

    Article  Google Scholar 

  27. Sadiq, M.T., Yu, X., Yuan, Z., Aziz, M.Z.: Motor imagery BCI classification based on novel two-dimensional modelling in empirical wavelet transform. Electron. Lett. 56(25), 1367–1369 (2020)

    Article  Google Scholar 

  28. Selim, S., Tantawi, M.M., Shedeed, H.A., Badr, A.: A CSP\(\backslash \)-BA-SVM approach for motor imagery BCI system. IEEE Access 6, 49192–49208 (2018)

    Google Scholar 

  29. Shajil, N., Mohan, S., Srinivasan, P., Arivudaiyanambi, J., Murrugesan, A.A.: Multiclass classification of spatially filtered motor imagery EEG signals using convolutional neural network for BCI based applications. J. Med. Biol. Eng. 40(5), 663–672 (2020)

    Article  Google Scholar 

  30. Sharanreddy, M., Kulkarni, P.: Detection of primary brain tumor present in EEG signal using wavelet transform and neural network. Int. J. Biol. Med. Res. 4(1), 2855–9 (2013)

    Google Scholar 

  31. Silvoni, S., et al.: Brain-computer interface in stroke: a review of progress. Clin. EEG Neurosci. 42(4), 245–252 (2011)

    Article  Google Scholar 

  32. Solomon, O.M., Jr.: PSD computations using Welch’s method. NASA STI/Recon Technical Report N, vol. 92, p. 23584 (1991)

    Google Scholar 

  33. de Souza, G.H., Bernardino, H.S., Vieira, A.B.: Single electrode energy on clinical brain-computer interface challenge. Biomed. Signal Process. Control 70, 102993 (2021)

    Article  Google Scholar 

  34. de Souza, G.H., Bernardino, H.S., Vieira, A.B., Barbosa, H.J.C.: Differential evolution based spatial filter optimization for brain-computer interface. In: Proceedings of the ACM Genetic and Evolutionary Computation Conference, pp. 1165–1173 (2019)

    Google Scholar 

  35. de Souza, G.H., Bernardino, H.S., Vieira, A.B., Barbosa, H.J.C.: Genetic programming for feature extraction in motor imagery brain-computer interface. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds.) EPIA 2021. LNCS (LNAI), vol. 12981, pp. 227–238. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86230-5_18

    Chapter  Google Scholar 

  36. Staffa, M., Giordano, M., Ficuciello, F.: A WiSARD network approach for a BCI-based robotic prosthetic control. Int. J. Soc. Robot. 12(3), 749–764 (2020)

    Article  Google Scholar 

  37. Stinear, C.M., Lang, C.E., Zeiler, S., Byblow, W.D.: Advances and challenges in stroke rehabilitation. Lancet Neurol. 19(4), 348–360 (2020)

    Article  Google Scholar 

  38. Tangermann, M., et al.: Review of the BCI competition IV. Front. Neurosci. 6(55) (2012)

    Google Scholar 

  39. Tzallas, A.T., et al.: EEG classification and short-term epilepsy prognosis using brain computer interface software. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), pp. 349–353. IEEE (2017)

    Google Scholar 

  40. Vansteensel, M.J., Jarosiewicz, B.: Brain-computer interfaces for communication. In: Handbook of Clinical Neurology, vol. 168, pp. 67–85. Elsevier (2020)

    Google Scholar 

  41. Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767–791 (2002)

    Article  Google Scholar 

  42. Yoshioka, M., Inoue, T., Ozawa, J.: Brain signal pattern of engrossed subjects using near infrared spectroscopy (NIRS) and its application to tv commercial evaluation. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2012)

    Google Scholar 

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Authors and Affiliations

  1. Federal University of Juiz de Fora, Juiz de Fora, Brazil

    Gabriel Henrique de Souza, Gabriel Oliveira Faria, Luciana Paixão Motta, Heder Soares Bernardino & Alex Borges Vieira

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  1. Gabriel Henrique de Souza
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  2. Gabriel Oliveira Faria
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  3. Luciana Paixão Motta
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  4. Heder Soares Bernardino
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  5. Alex Borges Vieira
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Correspondence to Gabriel Henrique de Souza .

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Editors and Affiliations

  1. Federal University of Rio Grande do Norte, Natal, Brazil

    João Carlos Xavier-Junior

  2. Federal University of Bahia, Salvador, Brazil

    Ricardo Araújo Rios

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de Souza, G.H., Faria, G.O., Motta, L.P., Bernardino, H.S., Vieira, A.B. (2022). Feature Extraction for a Genetic Programming-Based Brain-Computer Interface. In: Xavier-Junior, J.C., Rios, R.A. (eds) Intelligent Systems. BRACIS 2022. Lecture Notes in Computer Science(), vol 13653. Springer, Cham. https://doi.org/10.1007/978-3-031-21686-2_10

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  • DOI: https://doi.org/10.1007/978-3-031-21686-2_10

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