Abstract
This paper presents a class of noise reduction techniques for EEG signals. The noise reduction is very important for subsequent EEG processing tasks. The suggested techniques are extended from application in speech processing to application in EEG signals due to the common nature of low frequency of both types of signals. These techniques are spectral subtraction, Wiener filtering, adaptive Wiener filtering, and Discrete Wavelet Transform (DWT). A comparison between different techniques is presented. Simulation results are used to compare between the different denoising techniques. Four metrics are used to evaluate the different denoising techniques: signal -to-noise ratio (SNR), segmental signal-to-noise ratio (SNRseg), spectral distortion (SD), and log likelihood ratio (LLR).
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