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Front. Neurosci. | doi: 10.3389/fnins.2018.00076

Using Support Vector Machine on EEG for Advertisement Impact Assessment

 Zhen Wei1*, Chao Wu1,  Xiaoyi Wang2, Akara Supratak1, Hao Dong1 and Yike Guo1*
  • 1Imperial College London, United Kingdom
  • 2Zhejiang University, China

The advertising industry depends on an effective assessment of the impact of advertising as a key performance metric for their products. However, current assessment methods have relied on either indirect inference from observing changes in consumer behavior after the launch of an advertising campaign, which has long cycle times and requires an ad campaign to have already have been launched (often meaning costs having been sunk); or through surveys or focus groups, which have a potential for experimental biases, peer pressure, and other psychological and sociological phenomena that can reduce the effectiveness of the study. In this paper, we investigate a new approach to assess the impact of advertisement by utilizing low cost EEG headbands to record and assess the measurable impact of advertising on the brain. Our evaluation shows desired performance of our method based on user experiment with 11 recruited subjects after watching 220 different advertisements. We believe the proposed method can be further developed to a general and scalable methodology that can enable advertising agencies to assess impact rapidly, quantitatively, and without bias.

Keywords: EEG, SVM, Advertisement impact assessment, neuromarketing, machine learning

Received: 31 May 2017; Accepted: 30 Jan 2018.

Edited by:

Peter Lewinski, University of Oxford, United Kingdom

Reviewed by:

Xiaoli Li, Beijing Normal University, China
Dominika Basaj, Warsaw University of Technology, Poland  

Copyright: © 2018 Wei, Wu, Wang, Supratak, Dong and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Ms. Zhen Wei, Imperial College London, London, United Kingdom,
Prof. Yike Guo, Imperial College London, London, United Kingdom,