Wavelet transform to advance the quality of EEG signals in biomedical analysis

Among all physiological signals, present in the human body electroencephalogram (EEG) signals are accepted and productive in the application of mental state detection of a person. Electrical impulses produced by nerve firings in the brain diffuse through the head and can be measured by electrodes positioned on the scalp. EEG provides a coarse analysis of neural activity and has been utilized to non-invasively study cognitive processes and the composition of the brain. Analysis of EEG signals is useful for diagnosis of many neurological diseases such as epilepsy, tumors, problems associated with trauma. Proper diagnosis of disease requires faultless analysis of the EEG signals. Appropriate analysis requires the elimination of noise due to facial muscle movements, eye blinking, etc. The problem of denoising is quite varied due to variety of signals and noise. Discrete wavelet transform offers an effective solution for denoising nonstationary signals such as EEG due to its shrinkage property. In this paper, wavelet denoising is applied to EEG signals acquired during performing different mental tasks. The results are evaluated using the signal-to-noise ratio of the denoised signals.