Wavelet Transform based Weighted Averaging Technique for Visual Evoked Potential detection

Abstract

The statistical analysis and algorithms are used in digital signal processing to retrieve data found in sensors. Noise and artifacts contaminates the biomedical signals. One of the most significant issues of signal processing is the question of interpreting one signal from another. The ideal signal in most applications is not instantly noticeable or detectable. Instead, a distorted variant of the main signal becomes the detectable signal. The question of signal calculation is to extract the necessary signal from the mutilated copy in the best way possible. High additive noise, such as low evoked brain potential calculated in the high background of current EEG (electroencephalogram) signals, can cause the target signal to be obscured. The new technique, Wavelet Transform Weighted Ensemble Averaging outperforms Wavelet Transform Ensemble Averaging with better Signal to Noise (SNR) which has been recorded and tabulated for different Wavelet Techniques like Daubechies, Symlet & Bi-Orthogonal for various Tap weights.

Keywords: EEG, VEP, Ensemble Averaging, Wavelet Transform, Wavelet Transform Ensemble Averaging (WTEA), Wavelet Transform Weighted Ensemble Averaging (WTWEA) and SNR.