Time-series models can predict long periods of human temporal EEG responses to randomly alternating visual stimuli
by Richard Foster, Connor Delaney, Dean Krusienski, and Cheng Ly
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Brain activity with regularly flickering images has a rhythmic pattern that matches the flicker rate in electroencephalography (EEG) measurements. When the flicker speed changes randomly, the brain’s responses are more complicated. We recorded EEG signals in humans while viewing randomly alternating visual scenes. We tested how well statistical models capture and predict features of EEG. The models ranged from a simple one that used only past EEG values to more complex versions that added extra factors. Although all models considered were able to describe and predict key features of the EEG, the simplest model surprisingly performed as well—and sometimes better—than the more complicated ones. Therefore, simple models are worth considering.
Simpler statistical models can out perform complicated ones for capturing visual cortex EEG.
