Recent studies have demonstrated that the brain activity of a group of people can be used to forecast choices at the population level. In this study, we attempted to neuroforecast aggregate consumer behavior of Internet users. During our electroencephalography (EEG) and eye-tracking study, participants were exposed to 10 banners that were also used in the real digital marketing campaign. In the separate online study, we additionally collected self-reported preferences for the same banners. We explored the relationship between the EEG, eye-tracking, and behavioral indexes obtained in our studies and the banners’ aggregate efficiency provided by the large food retailer based on the decisions of 291,301 Internet users. An EEG-based engagement index (central beta/alpha ratio) significantly correlated with the aggregate efficiency of banners. Furthermore, our multiple linear regression models showed that a combination of eye-tracking, EEG and behavioral measurements better explained the market-level efficiency of banner advertisements than each measurement alone. Overall, our results confirm that neural signals of a relatively small number of individuals can forecast aggregate behavior at the population level.

This article describes the possibility of using expert systems (ES) in the framework of interactive electronic technical manuals. Invited to consider the benefits of diagnosis and prognosis of the technical condition of the product for the end user, also evaluate the capabilities of intellectualization accumulation of new declarative and procedural knowledge by experts. This can really enhance the intellectual component of the maintenance of modern and advanced equipment. The comparative analysis of the possibilities ES allowed to formulate requirements to clarify the criteria user requests. The article shows a block diagram of synthesis ES and IETM and its description. Named and described the types of knowledge of the expert system. The process of filling expert systems knowledge is automated.