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

© 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,

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.

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

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