Automatic identification of emotional patterns in audiovisual adverstising by biolectrical brian activity of an individual

Every day the consumer is exposed to lots of advertising messages, however businesses are uncertain of the emotions that such advertising generates in it, making it difficult to measure its impact. Understanding the consumer as the main body of an organization can create marketing strategies aligned with customer needs, systematic, objective and consistent to correctly orient the horizon of an organization. This paper develops and analyzes a system to assess the emotional affinity that an individual experiences when it is exposed to a particular broadcast advertising in terms of their bioelectrical brain activity (EEG signals). The system integrates a brain interface computer BCI and a set of adaptive vector models, which carry out the progressive identification emotional patterns in audiovisual advertising, from a series of visual emotional patterns learned reference models, and defined in terms of EEG signals. Results from the system, show the effectiveness and flexibility that have the integrated vectorial models to identify emotional patterns present in an audiovisual advertising.