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      Technological advancements and opportunities in Neuromarketing: a systematic review

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          Abstract

          Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.

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          A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC.
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                Author and article information

                Contributors
                frawnaque@umassd.edu
                mahmud_edu@bus.uiu.ac.bd
                farhat@iba-du.edu
                r.vaidyanathan@imperial.ac.uk
                tom.chau@utoronto.ca
                farhana.sarker@ulab.edu.bd
                mamun@cse.uiu.ac.bd
                Journal
                Brain Inform
                Brain Inform
                Brain Informatics
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                2198-4018
                2198-4026
                21 September 2020
                21 September 2020
                December 2020
                : 7
                : 1
                : 10
                Affiliations
                [1 ]GRID grid.443055.3, ISNI 0000 0001 2289 6109, Advanced Intelligent Multidisciplinary Systems Lab, Institute of Advanced Research, , United International University, ; Dhaka, Bangladesh
                [2 ]GRID grid.443055.3, ISNI 0000 0001 2289 6109, School of Business and Economics, , United International University, ; Dhaka, Bangladesh
                [3 ]GRID grid.8198.8, ISNI 0000 0001 1498 6059, Institute of Business Administration, , University of Dhaka, ; Dhaka, Bangladesh
                [4 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Department of Mechanical Engineering, , Imperial College London, ; London, United Kingdom
                [5 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Institute of Biomaterials & Biomedical Engineering, , University of Toronto, ; Toronto, Canada
                [6 ]GRID grid.443059.f, ISNI 0000 0004 0392 1542, Department of Computer Science and Engineering, , University of Liberal Arts Bangladesh, ; Dhaka, Bangladesh
                [7 ]GRID grid.443055.3, ISNI 0000 0001 2289 6109, Department of Computer Science and Engineering, , United International University, ; Dhaka, Bangladesh
                Article
                109
                10.1186/s40708-020-00109-x
                7505913
                32955675
                65e91bf5-bc58-4766-ab90-6a8e1b54ea9f
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 December 2019
                : 14 August 2020
                Funding
                Funded by: Institute of Advanced Research, United International University
                Award ID: IAR/01/19/SE/10
                Award Recipient :
                Categories
                Review
                Custom metadata
                © The Author(s) 2020

                neuromarketing,neural recording,machine learning algorithm,brain computer interface,marketing

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