Using Support Vector Machine on EEG for Advertisement Impact Assessment
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 campaig...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2018-03-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fnins.2018.00076/full |
| _version_ | 1818527877241503744 |
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| author | Zhen Wei Chao Wu Chao Wu Xiaoyi Wang Akara Supratak Pan Wang Yike Guo |
| author_facet | Zhen Wei Chao Wu Chao Wu Xiaoyi Wang Akara Supratak Pan Wang Yike Guo |
| author_sort | Zhen Wei |
| collection | DOAJ |
| description | 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 the desired performance of our method based on user experiment with 30 recruited subjects after watching 220 different advertisements. We believe the proposed SVM method can be further developed to a general and scalable methodology that can enable advertising agencies to assess impact rapidly, quantitatively, and without bias. |
| first_indexed | 2024-12-11T06:42:10Z |
| format | Article |
| id | doaj.art-28c50761b5bf45b3a01fcbd1ca6df51e |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-11T06:42:10Z |
| publishDate | 2018-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-28c50761b5bf45b3a01fcbd1ca6df51e2022-12-22T01:17:11ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2018-03-011210.3389/fnins.2018.00076284757Using Support Vector Machine on EEG for Advertisement Impact AssessmentZhen Wei0Chao Wu1Chao Wu2Xiaoyi Wang3Akara Supratak4Pan Wang5Yike Guo6Data Science Institute, Imperial College, London, United KingdomData Science Institute, Imperial College, London, United KingdomSchool of Public Affairs, Zhejiang University, Hangzhou, ChinaSchool of Management, Zhejiang University, Hangzhou, ChinaData Science Institute, Imperial College, London, United KingdomData Science Institute, Imperial College, London, United KingdomData Science Institute, Imperial College, London, United KingdomThe 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 the desired performance of our method based on user experiment with 30 recruited subjects after watching 220 different advertisements. We believe the proposed SVM method can be further developed to a general and scalable methodology that can enable advertising agencies to assess impact rapidly, quantitatively, and without bias.http://journal.frontiersin.org/article/10.3389/fnins.2018.00076/fullEEGSVMadvertisement impact assessmentneuromarketingmachine learning |
| spellingShingle | Zhen Wei Chao Wu Chao Wu Xiaoyi Wang Akara Supratak Pan Wang Yike Guo Using Support Vector Machine on EEG for Advertisement Impact Assessment Frontiers in Neuroscience EEG SVM advertisement impact assessment neuromarketing machine learning |
| title | Using Support Vector Machine on EEG for Advertisement Impact Assessment |
| title_full | Using Support Vector Machine on EEG for Advertisement Impact Assessment |
| title_fullStr | Using Support Vector Machine on EEG for Advertisement Impact Assessment |
| title_full_unstemmed | Using Support Vector Machine on EEG for Advertisement Impact Assessment |
| title_short | Using Support Vector Machine on EEG for Advertisement Impact Assessment |
| title_sort | using support vector machine on eeg for advertisement impact assessment |
| topic | EEG SVM advertisement impact assessment neuromarketing machine learning |
| url | http://journal.frontiersin.org/article/10.3389/fnins.2018.00076/full |
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