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Analysing Big Brain Signal Data for Advanced Brain Computer Interface System

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Databases Theory and Applications (ADC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13459))

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Abstract

The modern commercial industry is increasingly dependent on the analysis of vast data records. Analysing big electroencephalogram (EEG) signal data (called brain signal data) plays an important role in a wide variety of applications such as healthcare practices, brain computer interface (BCI) systems, innovative education, privacy and security, and biometrics. The key objective of this paper is to establish a methodological framework for identifying communicative intentions of motor disabled people from EEG data for application in BCI systems. The proposed framework is designed based on common spatial pattern (CSP) data method and optimized ensemble (OE) machine learning method for the application of BCI technologies. The CSP method is used for discovering important features from EEG data and finally the extracted features are fed as an input to optimized ensemble (OE) method. The proposed method was tested on BCI Competition III dataset IVa, which contains motor imagery-based EEG signal data. The experimental results show that our proposed method can handle brain signal big data for identifying communicative intentions for an advanced BCI system. We compared the performance of our proposed method with several other existing methods. In comparison with other established methods, our method achieves higher classification accuracy performance. This research assists the experts in processing and analysing EEG signals for BCI applications. It also supports technologists to create a new EEG data analyser for BCI systems.

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References

  1. AlHinai, N.: Introduction to biomedical signal processing and artificial intelligence. In: Biomedical Signal Processing and Artificial Intelligence in Healthcare, pp. 1–28. Elsevier (2020)

    Google Scholar 

  2. Alvi, A.M., Siuly, S., Wang, H.: Developing a deep learning based approach for anomalies detection from EEG data. In: Zhang, W., Zou, L., Maamar, Z., Chen, L. (eds.) WISE 2021. LNCS, vol. 13080, pp. 591–602. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-90888-1_45

    Chapter  Google Scholar 

  3. Alvi, A.M., Siuly, S., Wang, H.: A long short-term memory based framework for early detection of mild cognitive impairment from EEG signals. In: IEEE Transactions on Emerging Topics in Computational Intelligence (2022)

    Google Scholar 

  4. Alvi, A.M., Siuly, S., Wang, H., Wang, K., Whittaker, F.: A deep learning based framework for diagnosis of mild cognitive impairment. Knowl. Based Syst. 248, 108815 (2022)

    Article  Google Scholar 

  5. Chatterjee, R., Datta, A., Sanyal, D.K.: Ensemble learning approach to motor imagery EEG signal classification. In: Machine Learning in Bio-Signal Analysis and Diagnostic Imaging, pp. 183–208. Elsevier (2019)

    Google Scholar 

  6. Chaudhary, P., Agrawal, R.: Sensory motor imagery EEG classification based on non-dyadic wavelets using dynamic weighted majority ensemble classification. Intell. Decis. Technol. 15(1), 33–43 (2021)

    Article  Google Scholar 

  7. Chaudhary, S., Taran, S., Bajaj, V., Siuly, S.: A flexible analytic wavelet transform based approach for motor-imagery tasks classification in BCI applications. Comput. Methods Programs Biomed. 187, 105325 (2020)

    Article  Google Scholar 

  8. Cherloo, M.N., Amiri, H.K., Daliri, M.R.: Ensemble regularized common spatio-spectral pattern (ensemble RCSSP) model for motor imagery-based EEG signal classification. Comput. Biol. Med. 135, 104546 (2021)

    Article  Google Scholar 

  9. Golmohammadi, M., Torbati, A.H.H.N., de Diego, S.L., Obeid, I., Picone, J.: Automatic analysis of EEGS using big data and hybrid deep learning architectures. Front. Human Neurosci. 13, 76 (2019)

    Article  Google Scholar 

  10. Graham, S., et al.: Artificial intelligence for mental health and mental illnesses: an overview. Current Psych. Rep. 21(11), 1–18 (2019)

    Article  Google Scholar 

  11. Ince, N.F., Goksu, F., Tewfik, A.H., Arica, S.: Adapting subject specific motor imagery EEG patterns in space-time-frequency for a brain computer interface. Biomed. Signal Process. Control 4(3), 236–246 (2009)

    Article  Google Scholar 

  12. Li, J.-Y., Du, K.-J., Zhan, Z.-H., Wang, H., Zhang, J.: Distributed differential evolution with adaptive resource allocation. IEEE Trans. Cybern. (2022)

    Google Scholar 

  13. Li, Y., et al.: Clustering technique-based least square support vector machine for EEG signal classification. Comput. Methods Programs Biomed. 104(3), 358–372 (2011)

    Article  Google Scholar 

  14. Miao, Y., et al.: Learning common time-frequency-spatial patterns for motor imagery classification. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 699–707 (2021)

    Article  Google Scholar 

  15. Miao, Y., Yin, F., Zuo, C., Wang, X., Jin, J.: Improved RCSP and adaboost-based classification for motor-imagery BCI. In: 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp. 1–5. IEEE (2019)

    Google Scholar 

  16. Milne, E.: Increased intra-participant variability in children with autistic spectrum disorders: evidence from single-trial analysis of evoked EEG. Front. Psychol. 2, 51 (2011)

    Article  Google Scholar 

  17. Pandey, D., Wang, H., Yin, X., Wang, K., Zhang, Y., Shen, J.: Automatic breast lesion segmentation in phase preserved DCE-MRIS. Health Inf. Sci. Syst. 10(1), 1–19 (2022)

    Article  Google Scholar 

  18. Park, Y., Chung, W.: Optimal channel selection using covariance matrix and cross-combining region in EEG-based BCI. In: 2019 7th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–4. IEEE (2019)

    Google Scholar 

  19. Rashid, M., et al.: The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace K-NN. Peer J. Comput. Sci. 7, e374 (2021)

    Article  Google Scholar 

  20. Renuga Devi, K., Hannah Inbarani, H.: Neighborhood based decision theoretic rough set under dynamic granulation for BCI motor imagery classification. J. Multimod. User Interfaces 15(3), 301–321 (2021)

    Article  Google Scholar 

  21. Sadiq, M.T., et al.: Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework. Comput. Biol. Med. 143, 105242 (2022)

    Article  Google Scholar 

  22. Sadiq, M.T., Siuly, S., Ur Rehman, A.: Evaluation of power spectral and machine learning techniques for the development of subject-specific BCI. In: Artificial Intelligence-Based Brain-Computer Interface, pp. 99–120. Elsevier (2022)

    Google Scholar 

  23. Sadiq, M.T., Yu, X., Yuan, Z., Aziz, M.Z., Siuly, S., Ding, W.: Toward the development of versatile brain-computer interfaces. IEEE Trans. Artif. Intell. 2(4), 314–328 (2021)

    Article  Google Scholar 

  24. Sarki, R., Ahmed, K., Wang, H., Zhang, Y., Wang, K.: Automated detection of COVID-19 through convolutional neural network using chest x-ray images. PLOS ONE 17(1), e0262052 (2022)

    Article  Google Scholar 

  25. Sarki, R., Ahmed, K., Wang, H., Zhang, Y.,Wang, K.: Convolutional neural network for multi-class classification of diabetic eye disease. EAI Endorsed Trans. Scalab. Inf. Syst. p. e15 (2022)

    Google Scholar 

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

    Article  Google Scholar 

  27. Siuly, S., Li, Y.: Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 20(4), 526–538 (2012)

    Article  Google Scholar 

  28. Siuly, S., Li, Y.: Discriminating the brain activities for brain-computer interface applications through the optimal allocation-based approach. Neural Comput. App. 26(4), 799–811 (2015)

    Article  Google Scholar 

  29. Siuly, S., Li, Y., Zhang, Y.: EEG signal analysis and classification. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 141–144 (2016)

    Google Scholar 

  30. Tiwari, A., Chaturvedi, A.: A novel channel selection method for BCI classification using dynamic channel relevance. IEEE Access 9, 126698–126716 (2021)

    Article  Google Scholar 

  31. Wang, H., Zhang, Y., et al.: Detection of motor imagery EEG signals employing Naïve Bayes based learning process. Measurement 86, 148–158 (2016)

    Article  Google Scholar 

  32. Wang, Y., Gao, S., Gao, X.: Common spatial pattern method for channel selection in motor imagery based brain-computer interface. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 5392–5395. IEEE (2006)

    Google Scholar 

  33. Wei, W., Gao, X., Hong, B., Gao, S.: Classifying single-trial EEG during motor imagery by iterative spatio-spectral patterns learning (ISSPL). IEEE Trans. Biomed. Eng. 55(6), 1733–1743 (2008)

    Article  Google Scholar 

  34. Yin, J., Tang, M.J., Cao, J., Wang, H., You, M., Lin, Y.: Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning. World Wide Web 25(1), 401–423 (2022)

    Article  Google Scholar 

  35. Yong, X., Ward, R.K., Birch, G.E.: Sparse spatial filter optimization for EEG channel reduction in brain-computer interface. In: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 417–420. IEEE (2008)

    Google Scholar 

  36. Zhang, R., Peng, X., Guo, L., Zhang, Y., Li, P., Yao, D.: Z-score linear discriminant analysis for EEG based brain-computer interfaces. PLOS ONE 8(9), e74433 (2013)

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC, Australia

    Taslima Khanam, Siuly Siuly & Hua Wang

Authors
  1. Taslima Khanam
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  2. Siuly Siuly
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  3. Hua Wang
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Corresponding author

Correspondence to Taslima Khanam .

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Editors and Affiliations

  1. The University of Queensland, Brisbane, QLD, Australia

    Wen Hua

  2. Victoria University, Footscray, VIC, Australia

    Hua Wang

  3. The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China

    Lei Li

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Khanam, T., Siuly, S., Wang, H. (2022). Analysing Big Brain Signal Data for Advanced Brain Computer Interface System. In: Hua, W., Wang, H., Li, L. (eds) Databases Theory and Applications. ADC 2022. Lecture Notes in Computer Science, vol 13459. Springer, Cham. https://doi.org/10.1007/978-3-031-15512-3_8

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  • DOI: https://doi.org/10.1007/978-3-031-15512-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15511-6

  • Online ISBN: 978-3-031-15512-3

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