@article{MAKHILLJEAS2018131116319,
    title = {Detection and Separation of EEG Artifacts Using Wavelet Transform},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {11},
    pages = {4165-4172},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.4165.4172},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.4165.4172},
    author = {R. and},
    keywords = {Electroencephalography,multichannel estimation,wavelet transform,processing,mean square error,signal to noise ratio},
    abstract = {Bio-medical signal processing is one of the most important techniques of multichannel sensor
network and it has a substantial concentration in medical application. However, the real time and recorded
signals in multisensory instruments contains different and huge amount of noise and great work has been
completed in developing most favorable structures for estimating the signal source from the noisy signal in
multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output
signal through the Wide-Sense-Stationary (WSS) process with the help of time invariant filters. In this process,
the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the
non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure
to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary
signals but wavelet provide any possible way to approach multichannel signal processing. Based on the basic
structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal
coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used
for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise
reduction technique in VLSI to evaluate their parameters such as area utilization, power dissipation and
computation time.}
    }