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EEG-Based BCIs for Elderly Rehabilitation Enhancement Exploiting Artificial Data

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AIxIA 2021 – Advances in Artificial Intelligence (AIxIA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13196))

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Abstract

The ageing process may lead to cognitive and physical impairments, which may affect elderly everyday life. In recent years, the use of Brain Computer Interfaces (BCIs) based on Electroencephalography (EEG) has revealed to be particularly effective to promote and enhance rehabilitation procedures, especially by exploiting motor imagery experimental paradigms. Moreover, BCIs seem to increase patients’ engagement and have proved to be reliable tools for elderly overall wellness improvement. However, EEG signals usually present a low signal-to-noise ratio and can be recorded for a limited time. Thus, irrelevant information and faulty or insufficient samples could affect the BCI performance. Introducing a methodology that allows the extraction of informative components from the EEG signal while maintaining its intrinsic characteristics, may provide a solution to the described issues: noisy data may be avoided by having only relevant components and combining relevant components may represent a good strategy to substitute or augment the data without requiring long or repeated EEG recordings. To this end, in this work the EEG signal decomposition by means of multivariate empirical mode decomposition is proposed to obtain its oscillatory modes, called Intrinsic Mode Functions (IMFs). Subsequently, a novel procedure for relevant IMF selection based on the IMF time-frequency representation and entropy is provided. After having verified the reliability of the EEG signal reconstruction with the relevant IMFs only, the relevant IMFs are combined to produce new artificial data and provide new samples to use for BCI training.

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Notes

  1. 1.

    The original code is available at https://github.com/ffbear1993/DR-EMD.

  2. 2.

    https://physionet.org/content/eegmmidb/1.0.0/.

  3. 3.

    The detailed results and relative tables are available at https://github.com/asaibn/AIxIA2021.

References

  1. Belkacem, A.N., Jamil, N., Palmer, J.A., Ouhbi, S., Chen, C.: Brain computer interfaces for improving the quality of life of older adults and elderly patients. Front. Neurosci. 14, 692 (2020)

    Article  Google Scholar 

  2. Boutana, D., Benidir, M., Barkat, B.: On the selection of intrinsic mode function in emd method: application on heart sound signal. In: 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010), pp. 1–5. IEEE (2010)

    Google Scholar 

  3. Bueno-López, M., Muñoz-Gutiérrez, P.A., Giraldo, E., Molinas, M.: Analysis of epileptic activity based on brain mapping of EEG adaptive time-frequency decomposition. In: Wang, S., Yamamoto, V., Su, J., Yang, Y., Jones, E., Iasemidis, L., Mitchell, T. (eds.) BI 2018. LNCS (LNAI), vol. 11309, pp. 319–328. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05587-5_30

    Chapter  Google Scholar 

  4. Carelli, L., et al.: Brain-computer interface for clinical purposes: cognitive assessment and rehabilitation. BioMed Res. Int. 2017 (2017)

    Google Scholar 

  5. Chen, X., Xu, X., Liu, A., McKeown, M.J., Wang, Z.J.: The use of multivariate EMD and CCA for denoising muscle artifacts from few-channel EEG recordings. IEEE Trans. Instrum. Meas. 67(2), 359–370 (2017)

    Article  Google Scholar 

  6. Cohen, M.X.: A better way to define and describe Morlet wavelets for time-frequency analysis. NeuroImage 199, 81–86 (2019)

    Article  Google Scholar 

  7. Dinarès-Ferran, J., Ortner, R., Guger, C., Solé-Casals, J.: A new method to generate artificial frames using the empirical mode decomposition for an EEG-based motor imagery BCI. Front. Neurosci. 12, 308 (2018)

    Article  Google Scholar 

  8. Gallego-Jutglà, E., Solé-Casals, J., Rutkowski, T.M., Cichocki, A.: Application of Multivariate Empirical Mode Decomposition for Cleaning Eye Blinks Artifacts from EEG Signals. In: IJCCI (NCTA), pp. 455–460 (2011)

    Google Scholar 

  9. Gallego Jutglà, E., et al.: New signal processing and machine learning methods for EEG data analysis of patients with Alzheimer’s disease. Ph.D. thesis, Universitat de Vic-Universitat Central de Catalunya (2015)

    Google Scholar 

  10. Gaur, P., Pachori, R.B., Wang, H., Prasad, G.: An automatic subject specific intrinsic mode function selection for enhancing two-class EEG-based motor imagery-brain computer interface. IEEE Sens. J. 19(16), 6938–6947 (2019)

    Article  Google Scholar 

  11. Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)

    Article  Google Scholar 

  12. Gomez-Pilar, J., Corralejo, R., Nicolas-Alonso, L.F., Álvarez, D., Hornero, R.: Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly. Med. Biol. Eng. Comput. 54(11), 1655–1666 (2016). https://doi.org/10.1007/s11517-016-1454-4

    Article  Google Scholar 

  13. Gonzalez, R.: Digital image processing using Matlab-Gonzalez Woods & Eddins. pdf. Education (2004)

    Google Scholar 

  14. Hao, H., Wang, H., Rehman, N.: A joint framework for multivariate signal denoising using multivariate empirical mode decomposition. Signal Process. 135, 263–273 (2017)

    Article  Google Scholar 

  15. Herweg, A., Gutzeit, J., Kleih, S., Kübler, A.: Wheelchair control by elderly participants in a virtual environment with a brain-computer interface (BCI) and tactile stimulation. Biol. Psychol. 121, 117–124 (2016)

    Article  Google Scholar 

  16. Hu, M., Liang, H.: Search for information-bearing components in neural data. PLoS One 9(6), e99793 (2014)

    Article  Google Scholar 

  17. Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci. 454(1971), 903–995 (1998)

    Article  MathSciNet  Google Scholar 

  18. Kaiser, V., Bauernfeind, G., Kreilinger, A., Kaufmann, T., Kübler, A., Neuper, C., Müller-Putz, G.R.: Cortical effects of user training in a motor imagery based brain-computer interface measured by fNIRS and EEG. Neuroimage 85, 432–444 (2014)

    Article  Google Scholar 

  19. Komaty, A., Boudraa, A., Dare, D.: EMD-based filtering using the Hausdorff distance. In: 2012 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 000292–000297. IEEE (2012)

    Google Scholar 

  20. Lashgari, E., Liang, D., Maoz, U.: Data augmentation for deep-learning-based electroencephalography. J. Neurosci. Methods 346, 108885 (2020)

    Article  Google Scholar 

  21. Lee, H.K., Lee, J.H., Park, J.O., Choi, Y.S.: Data-driven data augmentation for motor imagery brain-computer interface. In: 2021 International Conference on Information Networking (ICOIN), pp. 683–686. IEEE (2021)

    Google Scholar 

  22. Liu, Y., et al.: A tensor-based scheme for stroke patients’ motor imagery EEG analysis in BCI-FES rehabilitation training. J. Neurosci. Methods 222, 238–249 (2014)

    Article  Google Scholar 

  23. Luo, Y., Lu, B.L.: EEG data augmentation for emotion recognition using a conditional Wasserstein GAN. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2535–2538. IEEE (2018)

    Google Scholar 

  24. Mane, R., Chouhan, T., Guan, C.: BCI for stroke rehabilitation: motor and beyond. J. Neural Eng. 17(4), 041001 (2020)

    Article  Google Scholar 

  25. Moctezuma, L.A., Molinas, M.: EEG-based subjects identification based on biometrics of imagined speech using EMD. In: Wang, S., et al. (eds.) BI 2018. LNCS (LNAI), vol. 11309, pp. 458–467. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05587-5_43

    Chapter  Google Scholar 

  26. Park, C., Looney, D., ur Rehman, N., Ahrabian, A., Mandic, D.P.: Classification of motor imagery BCI using multivariate empirical mode decomposition. IEEE Trans. Neural Syst. Rehabil. Eng. 21(1), 10–22 (2012)

    Article  Google Scholar 

  27. Piper, D., Schiecke, K., Pester, B., Benninger, F., Feucht, M., Witte, H.: Time-variant coherence between heart rate variability and EEG activity in epileptic patients: an advanced coupling analysis between physiological networks. New J. Phys. 16(11), 115012 (2014)

    Article  Google Scholar 

  28. Rato, R., Ortigueira, M.D., Batista, A.: On the HHT, its problems, and some solutions. Mech. Syst. Signal Process. 22(6), 1374–1394 (2008)

    Article  Google Scholar 

  29. Rehman, N., Mandic, D.P.: Multivariate empirical mode decomposition. Proc. R. Soc. A: Math. Phys. Eng. Sci. 466(2117), 1291–1302 (2010)

    Article  MathSciNet  Google Scholar 

  30. Rosenmai, P.: Using the median absolute deviation to find outliers. Eureka Stat. 25(11) (2013)

    Google Scholar 

  31. Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T.H., Faubert, J.: Deep learning-based electroencephalography analysis: a systematic review. J. Neural Eng. 16(5), 051001 (2019)

    Article  Google Scholar 

  32. Rüschendorf, L.: The Wasserstein distance and approximation theorems. Probab. Theory Relat. Fields 70(1), 117–129 (1985)

    Article  MathSciNet  Google Scholar 

  33. Saibene, A., Assale, M., Giltri, M.: Addressing digital divide and elderly acceptance of medical expert systems for healthy ageing. In: AIxAS@ AI* IA, pp. 14–24 (2020)

    Google Scholar 

  34. Saibene, A., Gasparini, F.: Cognitive and physiological response for health monitoring in an ageing population: a multi-modal System. In: El Yacoubi, S., Bagnoli, F., Pacini, G. (eds.) INSCI 2019. LNCS, vol. 11938, pp. 341–347. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34770-3_29

    Chapter  Google Scholar 

  35. Saibene, A., Gasparini, F.: Human-machine interaction: EEG electrode and feature selection exploiting evolutionary algorithms in motor imagery tasks. In: CENTRIC 2020 : The Thirteenth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services. pp. 8–14. IARIA, ThinkMind (2020)

    Google Scholar 

  36. Saibene, A., Gasparini, F.: GA for feature selection of EEG heterogeneous data. arXiv preprint arXiv:2103.07117 (2021)

  37. Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51(6), 1034–1043 (2004)

    Article  Google Scholar 

  38. Szczuko, P., Lech, M., Czyżewski, A.: Comparison of classification methods for EEG signals of real and imaginary motion. In: Stańczyk, U., Zielosko, B., Jain, L.C. (eds.) Advances in Feature Selection for Data and Pattern Recognition. ISRL, vol. 138, pp. 227–239. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67588-6_12

    Chapter  Google Scholar 

  39. Vaid, S., Singh, P., Kaur, C.: EEG signal analysis for BCI interface: a review. In: 2015 fifth international conference on advanced computing & communication technologies, pp. 143–147. IEEE (2015)

    Google Scholar 

  40. Vancea, M., Solé-Casals, J.: Population aging in the European information societies: towards a comprehensive research Agenda in eHealth innovations for elderly. Aging Dis. 7(4), 526 (2016)

    Article  Google Scholar 

  41. Wan, X., Zhang, K., Ramkumar, S., Deny, J., Emayavaramban, G., Ramkumar, M.S., Hussein, A.F.: A review on electroencephalogram based brain computer interface for elderly disabled. IEEE Access 7, 36380–36387 (2019)

    Article  Google Scholar 

  42. 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 

  43. Wu, Z., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1(01), 1–41 (2009)

    Article  Google Scholar 

  44. Zeiler, A., Faltermeier, R., Keck, I.R., Tomé, A.M., Puntonet, C.G., Lang, E.W.: Empirical mode decomposition-an introduction. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2010)

    Google Scholar 

  45. Zhang, K., et al.: Data augmentation for motor imagery signal classification based on a hybrid neural network. Sensors 20(16), 4485 (2020)

    Article  Google Scholar 

  46. Zhang, Z., et al.: A novel deep learning approach with data augmentation to classify motor imagery signals. IEEE Access 7, 15945–15954 (2019)

    Article  Google Scholar 

  47. Zhao, X., et al.: Classification of epileptic IEEG signals by CNN and data augmentation. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 926–930. IEEE (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Milano-Bicocca, Viale Sarca 336, 20126, Milan, Italy

    Aurora Saibene & Francesca Gasparini

  2. NeuroMI, Milan Center for Neuroscience, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126, Milan, Italy

    Aurora Saibene & Francesca Gasparini

  3. University of Vic-Central University of Catalonia, C de la Laura 13, 08500, Vic, Barcelona, Spain

    Jordi Solé-Casals

Authors
  1. Aurora Saibene
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  2. Francesca Gasparini
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  3. Jordi Solé-Casals
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Corresponding authors

Correspondence to Aurora Saibene , Francesca Gasparini or Jordi Solé-Casals .

Editor information

Editors and Affiliations

  1. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Stefania Bandini

  2. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Francesca Gasparini

  3. Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genova, Italy

    Viviana Mascardi

  4. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Matteo Palmonari

  5. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Giuseppe Vizzari

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Saibene, A., Gasparini, F., Solé-Casals, J. (2022). EEG-Based BCIs for Elderly Rehabilitation Enhancement Exploiting Artificial Data. In: Bandini, S., Gasparini, F., Mascardi, V., Palmonari, M., Vizzari, G. (eds) AIxIA 2021 – Advances in Artificial Intelligence. AIxIA 2021. Lecture Notes in Computer Science(), vol 13196. Springer, Cham. https://doi.org/10.1007/978-3-031-08421-8_25

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  • DOI: https://doi.org/10.1007/978-3-031-08421-8_25

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