Revealing the neural response to imperceptible peripheral flicker with machine learning.

Lighting in modern-day devices is often discrete. The sharp onsets and offsets of light are known to induce a steady-state visually evoked potential (SSVEP) in the electroencephalogram (EEG) at low frequencies. However, it is not well-known how the brain processes visual flicker at the threshold of conscious perception and beyond. To shed more light on this, we ran an EEG study in which we asked participants (N=6) to discriminate on a behavioral level between visual stimuli in which they perceived flicker and those that they perceived as constant wave light. We found that high frequency flicker which is not perceived consciously anymore still elicits a neural response in the corresponding frequency band of EEG, con-tralateral to the stimulated hemifield. The main contribution of this paper is to show the benefit of machine learning techniques for investigating this effect of subconscious processing: Common Spatial Pattern (CSP) filtering in combination with classification based on Linear Discriminant Analysis (LDA) could be used to reveal the effect for additional participants and stimuli, with high statistical significance. We conclude that machine learning techniques are a valuable extension of conventional neurophysiological analysis that can substantially boost the sensitivity to subconscious effects, such as the processing of imperceptible flicker.