Brain Dynamics Encoding from Visual Input during Free Viewing of Natural Videos

Neuroscientific studies have revealed that alpha oscillations in electroencephalography (EEG) signals play an active role in the cognitive aspects of human daily life. In this paper, we developed an artificial encoding model to mimic the dynamic brain visual processing system in terms of alpha oscillations. We analyzed the induced alpha power during free viewing of natural videos and extracted relevant features in a participant-independent way. Meanwhile, the visual characteristics that would trigger involuntary attention in the early stage of visual processing were extracted from videos and further employed to estimate the alpha fluctuations at every 0.5 s. We compared the encoding performances across 19 electrode channel locations and selected an optimal encoding model with a support vector regression. The results demonstrated a promising brain encoding model in EEG signals, that could further contribute to the development of brain-computer interface and visual design.