Abstract

This article proposes an improved method to collect data from electroencephalograms (EEG) used in neuromarketing; in other words, this study addresses neuroscientific consumer research to develop insights into customers’ affective responses to the stimuli of brands and products. There are now more affordable neuro-devices capturing the brainwave signals for various applications like design, marketing, and brain-computer interfaces. It is necessary to construct an algorithmic model that captures and analyzes data more effectively to facilitate the usage of such hardware. The traditional method is to measure the asymmetry index, that is, to subtract the natural log of the left alpha wave power from that of the right alpha wave power. However, this cannot indicate how alpha activities of the two separate hemispheres contribute to changes. Moreover, it cannot directly reflect alpha activation at a particular point of time and cannot determine when activation (if any) occurs after the presentation of a stimulus (like brand slogans). Based on analysis of the first derivative of alpha wave power, the proposed method effectively identifies alpha activation both visually and algorithmically, and it can identify the time of activation and the extent of activation precisely. The overall accuracy of the traditional method is 56.8 percent, while that of the suggested new method is 93.1 percent.