An Efficient Adaptive Learning Algorithm for EEG Analysis in Brain Computer Interface Applications

An Efficient Adaptive Learning Algorithm for EEG Analysis in Brain Computer Interface Applications

  IJETT-book-cover           
  
© 2021 by IJETT Journal
Volume-69 Issue-7
Year of Publication : 2021
Authors : M.V.V.S Prasad, T. Ranga Babu
DOI :  10.14445/22315381/IJETT-V69I7P232

How to Cite?

M.V.V.S Prasad, T. Ranga Babu, "An Efficient Adaptive Learning Algorithm for EEG Analysis in Brain Computer Interface Applications," International Journal of Engineering Trends and Technology, vol. 69, no. 7, pp. 255-262, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I7P232

Abstract
For brain signal enhancement several adaptive signal processing techniques are available. In clinical environments while doing electroencephalogram (EEG) records it encounter with several artifacts and it may affect brain activities. To get high resolution brain wave signals, new adaptive learning technique is proposed in health care monitoring applications. In this paper logarithmic normalized mean least square (LNLMS) algorithm is proposed then it is compared with conventional least mean square (LMS) algorithm. Input brain waves are contaminated with EMG and EMA artifacts, these artifacts are reduced by LNLMS algorithm. Further applied sign regressor, sign and sign sign variants to adaptive algorithm. Using these sign variants, computational complexity is also reduced. Among these three variants sign regressor based LNLMS (SR-LNLMS) algorithm is preferred because its low computational complexity and also it is best suited for health care monitoring applications. Experimental results show that proposed algorithm perform well by means of signal to noise ratio, excess mean square error with values of 17.6214dB and -34.5856dB respectively.

Keywords
Adaptive algorithm, Brain waves, EEG, Health care monitoring, Noise canceller.

Reference
[1] F. C. Morabito et al., Enhanced compressibility of EEG signal in Alzheimer’s disease patients, IEEE Sensors J., 13(9)(2013) 3255– 3262.
[2] S. Gao, Y. Wang, X. Gao, and B. Hong, Visual and auditory brain– computer interfaces, IEEE Trans. Biomed. Eng., 61(5)(2014) 1436– 1447,.
[3] T. Zarghami, H. S. Mir, and H. Al-Nashash, Transfer-function-based calibration of sparse EEG systems for brain source localization, IEEE Sensors J., 15(3)(2015) 1504–1514.
[4] Communicating risk in public health emergencies: A WHO guideline for emergency risk communication (ERC) policy and practice, ISBN 978-92-4-155020-8, World Health Organization, (2017).
[5] S. Makeig, C. Kothe, T. Mullen, N. B. Shamlo, Z. Zhang, and K. K. Delgado,Evolving signal processing for brain-computer interfaces, Proc.IEEE, vol. 100(13)(2012) 1567–1584.
[6] Juan Andrés Mucarquer, Pavel Prado, María-José Escobar, Wael El- Deredy, and Matías Zañartu, Improving EEG Muscle Artifact Removal With an EMG Array, IEEE Transactions on Instrumentation and Measurement, 69(3)(2020) 815-824.
[7] Manali Saini, Payal, and Udit Satija, An Effective and Robust Framework for Ocular Artifact Removal from Single-Channel EEG Signal Based on Variational Mode Decomposition, IEEE Sensors Journal, 20(1)(2020), 369-376.
[8] Soojin Lee, Martin J. McKeown, Z. Jane Wang, Xun Chen, Removal of High-Voltage Brain Stimulation Artifacts from Simultaneous EEG Recordings, IEEE Transactions on Biomedical Engineering, 66(1)(2019) 50-60.
[9] A. Nijholt and D. Tan, Brain-computer interfacing for intelligent systems, IEEE Intell. Syst., 23(3)(2008) 72–79.
[10] B. J. Lance, S. E. Kerick, A. J. Ries, K. S. Oie, and K. McDowell, Brain computer interface technologies in the coming decades, Proc. IEEE, 100(13)(2012) 1585–1599.
[11] J. B. F. Van Erp, F. Lotte, and M. Tangermann, Brain computer interfaces: Beyond medical applications, Computer, 45(4)(2012) 26– 34.
[12] G. Schalk and E. C. Leuthardt, Brain-computer interfaces using electrocorticographic signals, IEEE Rev. Biomed. Eng., 4(2011),140– 154.
[13] Y. Lingling, H. Leung, M. Plank, J. Snider, and H. Poizner, EEG Activity During Movement Planning Encodes Upcoming Peak Speed and Acceleration and Improves the Accuracy in Predicting Hand Kinematics, IEEE Journal of Biomedical and Health Informatics, 19(1)(2015) 22-28.
[14] Pranjali Gajbhiye, Rajesh Kumar Tripathy, Abhijit Bhattacharyya, Ram Bilas Pachori, Novel Approaches for the Removal of Motion Artifact from EEG Recordings, IEEE Sensors Journal, 19(22)(2019) 10600-10608.
[15] Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh, Automatic EEG eyeblink artefact identification and removal technique using independent component analysis in combination with support vector machines and denoising autoencoder, IET Signal Processing, 14(6)(2020) 396-405,.
[16] Xun Chen, Qiang Chen, Yu Zhang, Z. Jane Wang., A Novel EEMDCCA Approach to Removing Muscle Artifacts for Pervasive EEG, IEEE Sensors Journal, 19(19)(2020) 8420- 8431.
[17] G.V.S.Karthik, S. Y. Fathima, Md. Zia Ur Rahman, Sk.RafiAhamed, A. Lay-Ekuakille, Efficient Signal conditioning techniques for Brain activity in Remote Health Monitoring Network, IEEE Sensors Journal, 13(9)(2013) 3276-3283.
[18] B. Farhang-Boroujeny, Adaptive Filters-Theory and Applications. Chichester, U.K.: Wiley, (1998).
[19] M. Nagesh, Md. Zia Ur Rahman, A New ECG Signal Enhancement Strategy using Non- NegativeAlgorithms, International Journal of Control Theory and Applications 10(35)(2017) 323-333.
[20] Rajdeep Ghosh, Nidul Sinha, Saroj Kumar Biswas, Automated eye blink artefact removal from EEG using support vector machine and autoencoder, IET Signal Processing, 13(2)(2019) 141- 148.
[21] Xun Chen, Xueyuan Xu, Aiping Liu, Soojin Lee, Xiang Chen, Xu Zhang, Martin J. McKeown, Z. Jane Wang, Removal of muscle artifacts from the EEG: a review and recommendations, IEEE Sensors Journal, 19(14)(2019) 5353-5368,.
[22] A. Lay-Ekuakille, P. Vergallo, A. Trabacca, M. De Rinaldis, F. Angelillo, F. Conversano, and S. Casciaro, Low-frequency detection in ECG signals and joint EEG-ergospirometric measurements for precautionary diagnosis, measurement, J. Meas., 46(1)(2013) 97–107.
[23] N. Mammone, A. Lay-Ekuakille, C. Morabito, A. Massaro, S. Casciaro, and A. Trabacca, Analysis of absence seizure EEG via permutation entropy spatio-temporal clustering, Proc. IEEE Int. Symp. Med. Meas. Appl., (2011) 532–535.
[24] A. Lay-Ekuakille, P. Vergallo, G. Griffo, F. Conversano, S. Casciaro, S. Urooj, V. Bhateja, and A. Trabacca, Mutidimensional analysis ofEEG features using advanced spectral estimates for diagnosis accuracy, Proc. IEEE Int. Symp. Med. Meas. Appl., (2013).
[25] K. Mayyas and TyseerAboulnasr, Leaky LMS Algorithm: MSE Analysis for Gaussian Data, IEEE Transactions on Signal Processing, 45(4)(1997) 927-934.