Biorthogonal 3.1 Wavelet Enhancement of Brain Activities

Electroencephalographic (EEG) data collected from 12 participants were used in a study to identify the wavelet, from a set of standard wavelets, that would best separate the means of two conditions of visual cortex activity, with and without visual flow stimuli. Independent component analysis (ICA) was used to estimate visual cortex source activities from the EEG data. The biorthogonal 3.1 wavelet was found to best separate the conditions (relative to the other wavelets tested) and was characterized as having a broad spectrum, similar to a white noise process. The maximum average wavelet coefficients corresponded to the absence of visual flow rather than the presence of visual-flow stimuli. The opposite case was true for most other wavelets tested. Our results are consistent with the notion that the activities of the primary visual cortex during exposure to a blank screen may be considerably more random compared to those during visual-flow.