Multi-channel noise reduced Visual Evoked Potential analysis

In this paper, Principal Component Analysis (PCA) is used to reduce noise from multi-channel Visual Evoked Potential (VEP) signals. PCA is applied to reduce noise from multi-channel VEP signals because VEP signals are more correlated from one channel to another as compared to noise during visual perception. Emulated VEP signals contaminated with noise are used to show the noise reduction ability of PCA. These noise reduced VEP signals are analysed in the gamma spectral band to classify alcoholics and non-alcoholics with a Fuzzy ARTMAP (FA) neural network. A zero phase Butterworth digital filter is used to extract gamma band power in spectral range of 30 to 50 Hz from these noise reduced VEP signals. The results using 800 VEP signals give an average FA classification of 92.50% with the application of PCA and 83.33% without the application of PCA.