Efficient de-noising technique for electroencephalogram signal processing

An electroencephalogram (EEG) is a recording of various frequencies electrical activity in the brain. EEG signal very useful for diagnosis brain related diseases at early stage to prevent severe issues which may lead loss life. The raw captured through leads contain different type noises not susceptible diagnosis. In this paper, an efficient algorithm proposed process combat noise. To obtain noiseless data, likelihood test ratio applied interference computation block. converts data into segmented with nearly constant noise characteristics. This will aid detecting present tiny segment ensures proper denoising. processed compared database same person using principal component analysis (PCA) classifier. 99.01% identify and signal.