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Comparative Analysis of Signal Processing Techniques for Mental State Recognition in Brain-Computer Interfaces (BCI)

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A Correction to this article was published on 04 July 2023

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Abstract

The BCI (Brain-computer interface) is a new-age tool in which human or artificial body parts like prosthetic arms can be controlled by sensing the EEG signals. To understand the mental state of the brain, the study of EEG is very important but this technique has its own limitation, which has been identified in this work. Various signal-processing techniques of EEG have been studied to analyze the mental state of the brain. A novel cross-technique will be introduced to improve EEG signal processing techniques. The spatial and temporal resolution trade-off problem is the biggest challenge in EEG signal acquisition systems. This is resolved by taking the fMRI signal and then transforming it into an EEG signal by decomposition and comparing modules for better spatial resolution in the EEG signal. The main limitations like frequency overlapping and decomposition, of the feature extraction technique, have been investigated and an improvised general algorithm will be introduced in this proposed work. This work also touches briefly on different classification algorithms which follow different learning criteria and optimized techniques will be used to achieve better performance. The main  objective of this proposed work is the identification of the best-suited algorithm for each step of signal processing to understand the mental state of the brain, brain oscillation characteristics, and some old and new techniques trends companion for the analysis of signal processing techniques of the brain.

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Change history

  • 25 June 2023

    The original version of this article was revised: In this article the author name Narendra Kumar Shukla was incorrectly written as Naredra Kumar Shukla. The original article has been corrected.

  • 04 July 2023

    A Correction to this paper has been published: https://doi.org/10.1007/s11277-023-10574-2

References

  1. Wolpaw, J., & Wolpaw, E. W. (2012). Brain–Computer InterfacesPrinciples and Practice. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195388855.001.0001

    Book  Google Scholar 

  2. Lotte, F., Bougrain, L., Clerc, M. (1999) Electroencephalography (EEG)-based brain–computer interfaces, Wiley Encyclopedia of Electrical and Electronics Engineering, pp. 1–20.

  3. Keirn, Z. A., & Aunon, J. I. (1990). A new mode of communication between man and his surroundings, IEEE Trans. BioMedical Engineering, 37(12), 1209–1214.

    Google Scholar 

  4. Deshpande, G., Rangaprakash, D., Oeding, L., Cichocki, A., & Hu, X. P. (2017). A new generation of brain-computer interfaces driven by discovery of latent EEG-fMRI linkages using tensor decomposition. Frontiers in Neuroscience, 7(11), 246. https://doi.org/10.3389/fnins.2017.00246.PMID:28638316;PMCID:PMC5461249

    Article  Google Scholar 

  5. Penny, W. D., Roberts, S. J., Curran, E. A., & Stokes, M. J. (2000). EEG-based communication: A pattern recognition approach. IEEE Transactions on Rehabilitation Engineering, 8(2), 214–215.

    Article  Google Scholar 

  6. Anuragi, D. S., & Sisodia, R. B. (2020). Pachori, Automated alcoholism detection using fourier-bessel series expansion based empirical wavelet transform. IEEE Sensors Journal, 20, 4914–4924.

    Article  Google Scholar 

  7. Battista, B. M., Knapp, C., McGee, T., & Goebel, V. (2007). Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data. Geophysics, 72(2), H29–H37.

    Article  Google Scholar 

  8. Sun, S., & Zhang, Q. (2011). Multiple-view multiple-learner semi-supervised learning. Neural Processing Letters, 34, 229–240.

    Article  Google Scholar 

  9. Gupta, R., Khan, U., Singh, V. K., Tanveer, M., & Kumar, D. (2020). A novel approach for classification of mental tasks using multiview ensemble learning (MEL). Neurocomputing, 417, 558–584.

    Article  Google Scholar 

  10. Ranzani, R., Lambercy, O., Metzger, J. C., et al. (2020). Neurocognitive robot-assisted rehabilitation of hand function: A randomized control trial on motor recovery in subacute stroke. Journal of Neuroengineering and Rehabilitation, 17(1), 115.

    Article  Google Scholar 

  11. Prokopenko, S. V., Mozheyko, E. Y., Petrova, M. M., et al. (2013). Correction of post-stroke cognitive impairments using computer programs. Journal of the Neurological Sciences, 325(1–2), 148–153.

    Article  Google Scholar 

  12. Taylor, G. H., & Broomfield, N. M. (2013). Cognitive assessment and rehabilitation pathway for stroke (CARPS). Topics in Stroke Rehabilitation, 20(3), 270–282.

    Article  Google Scholar 

  13. Rosenfeld, J. P., Rudell, A. P., & Fox, S. S. (1969). Operant control of neural events in humans. Science, 165(3895), 821–823.

    Article  Google Scholar 

  14. Vidal, J. J. (1973). Toward direct brain-computer communication. Annual Review of Biophysics and Bioengineering, 2, 157–180.

    Article  Google Scholar 

  15. Pfurtscheller, G., & Aranibar, A. (1977). Event-related cortical desynchronization detected by power measurements of scalp EEG. Electroencephalography and Clinical Neurophysiology, 42(6), 817–826.

    Article  Google Scholar 

  16. Fiedler, L., Wöstmann, M., Graversen, C., Brandmeyer, A., Lunner, T., & Obleser, J. (2017). Single-channel in-ear-EEG detects the focus of auditory attention to concurrent tone streams and mixed speech. Journal of Neural Engineering, 14(3), 036020. https://doi.org/10.1088/1741-2552/aa66dd

    Article  Google Scholar 

  17. Kidmose, P., Looney, D., Ungstrup, M., Rank, M., & Mandic, D. P. (2013). A study of evoked potentials from ear-EEG. IEEE Transactions on Biomedical Engineering, 60(10), 2824–2830.

    Article  Google Scholar 

  18. Zyma, I., Tukaev, S., Seleznov, I., Kiyono, K., Popov, A., Chernykh, M., & Shpenkov, O. (2019). Electroencephalograms during mental arithmetic task performance. Data, 4(1), 14.

    Article  Google Scholar 

  19. Lotte, F. (2014). A Tutorial on EEG Signal Processing Techniques for Mental State Recognition inBrain Computer Interfaces. Springer.

    Google Scholar 

  20. Yuzheng, D., Zhang, L., Liu, W., Rao, C., Li, B., Nan, X., Li, Z., & Jiang, H. (2020). Effect of acupuncture treatment on post-stroke cognitive impairment: A randomized controlled trial. Medicine, 99(51), e23803. https://doi.org/10.1097/MD.0000000000023803

    Article  Google Scholar 

  21. Hara, T., Shanmugalingam, A., McIntyre, A., & Burhan, A. M. (2021). The effect of non-invasive brain stimulation (NIBS) on attention and memory function in stroke rehabilitation patients. A Systematic Review and Meta-Analysis. Diagnostics, 11(2), 227.

    Google Scholar 

  22. Maier, M., Ballester, B. R., Bañuelos, N. L., Oller, E. D., & Verschure, P. F. M. J. (2020). Adaptive conjunctive cognitive training (ACCT) in virtual reality for chronic stroke patients: a randomized controlled pilot trial. Journal of NeuroEngineering and Rehabilitation. https://doi.org/10.1186/s12984-020-0652-3

    Article  Google Scholar 

  23. Rak, R. J., Kołodziej, M., & Majkowski, A. (2012). Brain-computer interface as measurement and control system the review paper. Metrology and Measurement Systems, XIX(3), 427–444.

    Google Scholar 

  24. Antonio la O Serna de, J., & PaterninaZamora-MendezTripathyPachori, M. R. A. A. R. K. R. B. (2020). EEG-rhythm specific taylor–fourier filter bank implemented with O-splines for the detection of epilepsy using EEG signals. IEEE Sensors Journal, 20(12), 6542–6551. https://doi.org/10.1109/JSEN.2020.2976519

    Article  Google Scholar 

  25. Gupta, A., Agrawal, R. K., Kirar, J. S., Andreu-Perez, J., Ding, W. P., Lin, C. T., & Prasad, M. (2019). On the utility of power spectral techniques with feature selection techniques for effective mental task classification in noninvasive BCI. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 99, 1–13.

    Google Scholar 

  26. Liu, R., Yu, X., Wang, J., et al. (2020). Evaluation of the efficacy and safety of the use of acupuncture for the adjuvant treatment of patients with post-stroke cognitive impairment: Protocol for a randomized controlled trial. Trials, 21(1), 753.

    Article  Google Scholar 

  27. Sani, M. M., Norhazman, H., Omar, H. A., Zaini, N., & Ghani, S. A. (2014). Support vector machine for classification of stress subjects using EEG signals. In 2014 IEEE Conference on Systems, Process and Control (ICSPC 2014) (pp. 127-131). IEEE.

  28. Bhattacharyya, R. B., Pachori, A., & Upadhyay, U. R. (2017). Acharya, Tunable-q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Applied Sciences, 7, 385.

    Article  Google Scholar 

  29. Stoica, P., & Moses, R. L. (2005). Spectral Analysis of Signals. Prentice-Hall.

    Google Scholar 

  30. Sharma, R., Pachori, R. B., & Upadhyay, A. (2017). Automatic sleep stages classification based on iterative filtering of electroencephalogram signals. Neural Computing and Applications, 28, 2959–2978.

    Article  Google Scholar 

  31. Amin, H. U., Mumtaz, W., Subhani, A. R., Saad, M. N. M., & Malik, A. S. (2017). Classification of eeg signals based on pattern recognition approach. Frontiers in Computational Neuroscience, 11, 103.

    Article  Google Scholar 

  32. Rezaei, S., Tavakolian, K., & Naziripour, K. (2006). Comparison of five different classifiers for classification of mental tasks. In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference (pp. 6007-6010). IEEE.

  33. Zhiwei, L., & Minfen, S. (2007). Classification of mental task EEG signals using wavelet packet entropy and SVM. In 2007 8th International Conference on Electronic Measurement and Instruments (pp. 3-906). IEEE.

  34. Blackburn, D. J., Krishnan, K., Fox, L., Ballard, C., Burns, A., Ford, G. A., Mant, J., Passmore, P., Pocock, S., Reckless, J., Sprigg, N., Stewart, R., Wardlaw, J., & Bath, P. M. W. (2013). Prevention of decline in cognition after stroke trial (PODCAST): A study protocol for a factorial randomised controlled trial of intensive versus guideline lowering of blood pressure and lipids. Trials. https://doi.org/10.1186/1745-6215-14-401

    Article  Google Scholar 

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Authors do not have any financial interest.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, University of Allahabad, Prayagraj, Uttar Pradesh, India

    Pradeep Kumar Tiwari, Animesh Tripathi, Arunesh Dutt, Shiv Prakash & Narendra Kumar Shukla

  2. Department of Higher Education, Lucknow, Uttar Pradesh, India

    S. K. Yadav & Uttam K. Sharma

  3. King George’s Medical University, Lucknow, Uttar Pradesh, India

    Pratibha Dixit

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  1. S. K. Yadav
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Correspondence to Pradeep Kumar Tiwari.

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The original version of this article was revised: In this article the author name Narendra Kumar Shukla was incorrectly written as Naredra Kumar Shukla. The original article has been corrected.

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Yadav, S.K., Tiwari, P.K., Tripathi, A. et al. Comparative Analysis of Signal Processing Techniques for Mental State Recognition in Brain-Computer Interfaces (BCI). Wireless Pers Commun 131, 1569–1592 (2023). https://doi.org/10.1007/s11277-023-10514-0

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