A Visual Attention Monitor Based on Steady-State Visual Evoked Potential

Attention detection is important for many applications. Automatic determination of users' visual attention state is challenging because attention involves numerous complex and internal human cognitive functions. Behavioral observations, such as eye gaze or response to external stimuli, can provide clues for users' visual attention state; however, users' cognitive state cannot be easily known. Conventional electroencephalography-based methods detect attention by observing the dynamic changes in the frontal lobe of the brain, especially in the anterior cingulate cortex (ACC). However, that area in the brain is associated with many functions, some of which correlate with conscious experience but are not directly related to attention. In this paper, we design an attention monitoring system to detect whether the brain experiences a visual stimulus consciously. Our experiments verified the feasibility of our design, and the average classification rate ranged from 72% to 82%.