Estimation of Stimulus Time and Average Attention State Based on Collective Addition of Event-Related Electroencephalography

Brain-computer interface is mainly developed for clinical rehabilitation. Numerous studies have shown that it can also be applied to neuromarketing to assist customers in making decisions. By identifying the P300 component of the event-related potentials (ERPs), it can be known whether the target commodity or target stimuli is interesting to the consumer. However, when the target stimuli appear more frequently and people’s responses to stimuli vary, it is challenging to locate the target stimuli based on the P300 in practical applications. Moreover, a significant P300 component can only be obtained by stacking and averaging multiple ERPs in normal conditions. In this study, we propose a group electroencephalogram processing method to estimate the timing of evoked stimulus appearance without compromising real-time performance using convolutional neural networks. In addition, this method can be used to estimate the group’s attention to the target and standard stimulus. The results show that the effectiveness of the proposed processing method for stimuli presentation time estimation and group attention state estimation are 87.10 % and 96.55 %, respectively.