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      An fNIRS-based investigation of visual merchandising displays for fashion stores

      1 , 2 , 3 , 1 , 3 , *

      PLoS ONE

      Public Library of Science

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          Abstract

          This paper investigates a brain-based approach for visual merchandising display (VMD) in fashion stores. In marketing, VMD has become a research topic of interest. However, VMD research using brain activation information is rare. We examine the hemodynamic responses (HRs) in the prefrontal cortex (PFC) using functional near-infrared spectroscopy (fNIRS) while positive/negative displays of four stores (menswear, womenswear, underwear, and sportswear) are shown to 20 subjects. As features for classifying the HRs, the mean, variance, peak, skewness, kurtosis, t-value, and slope of the signals for a 20-sec time window for the activated channels are analyzed. Linear discriminant analysis is used for classifying two-class (positive and negative displays) and four-class (four fashion stores) models. PFC brain activation maps based on t-values depicting the data from the 16 channels are provided. In the two-class classification, the underwear store had the highest average classification result of 67.04%, whereas the menswear store had the lowest value of 64.15%. Men’s classification accuracy for the underwear stores with positive and negative displays was 71.38%, whereas the highest classification accuracy obtained by women for womenswear stores was 73%. The average accuracy over the 20 subjects for positive displays was 50.68%, while that of negative displays was 51.07%. Therefore, these findings suggest that human brain activation is involved in the evaluation of the fashion store displays. It is concluded that fNIRS can be used as a brain-based tool in the evaluation of fashion stores in a daily life environment.

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          Most cited references 54

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          Machine learning classifiers and fMRI: a tutorial overview.

          Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviours and other variables of interest from fMRI data and thereby show the data contain information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of 'is there information about a variable of interest' (pattern discrimination), classifiers can be used to tackle other classes of question, namely 'where is the information' (pattern localization) and 'how is that information encoded' (pattern characterization).
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            NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy.

            Near infrared spectroscopy (NIRS) is a non-invasive method to measure brain activity via changes in the degree of hemoglobin oxygenation through the intact skull. As optically measured hemoglobin signals strongly correlate with BOLD signals, simultaneous measurement using NIRS and fMRI promises a significant mutual enhancement of temporal and spatial resolutions. Although there exists a powerful statistical parametric mapping tool in fMRI, current public domain statistical tools for NIRS have several limitations related to the quantitative analysis of simultaneous recording studies with fMRI. In this paper, a new public domain statistical toolbox known as NIRS-SPM is described. It enables the quantitative analysis of NIRS signal. More specifically, NIRS data are statistically analyzed based on the general linear model (GLM) and Sun's tube formula. The p-values are calculated as the excursion probability of an inhomogeneous random field on a representation manifold that is dependent on the structure of the error covariance matrix and the interpolating kernels. NIRS-SPM not only enables the calculation of activation maps of oxy-, deoxy-hemoglobin and total hemoglobin, but also allows for the super-resolution localization, which is not possible using conventional analysis tools. Extensive experimental results using finger tapping and memory tasks confirm the viability of the proposed method.
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              Near infrared spectroscopy (NIRS): a new tool to study hemodynamic changes during activation of brain function in human adults.

              In healthy human adults, cerebral concentrations of oxygenated hemoglobin ([HbO2]) and deoxygenated hemoglobin ([HbR]) were assessed during brain activation using near infrared spectroscopy (NIRS). Measurements were made either in the frontal cortex (n = 10) during performance of cognitive tasks or in the occipital cortex (n = 6) during visual stimulation (flash-light exposure, picture observation). The typical findings during brain activation were an increase in [HbO2] and a decrease in [HbR]. We demonstrate that these findings are not due to alterations in skin blood flow. NIRS is a simple bedside technique for the assessment of hemodynamic alterations accompanying brain activation.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: Writing – original draft
                Role: SupervisionRole: ValidationRole: Visualization
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                11 December 2018
                2018
                : 13
                : 12
                PONE-D-18-24262
                10.1371/journal.pone.0208843
                6289445
                30533055
                © 2018 Liu et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Counts
                Figures: 8, Tables: 6, Pages: 19
                Product
                Funding
                Funded by: The National Research Foundation of Korea under the auspices of the Ministry of Science and ICT, Republic of Korea
                Award ID: NRF-2017R1A4A1015627
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100004543, China Scholarship Council;
                Award ID: 201408260012
                Award Recipient :
                This research was supported by the China Scholarship Council (grant no. 201408260012) and the National Research Foundation of Korea under the auspices of the Ministry of Science and ICT, Republic of Korea (grant no. NRF-2017R1A4A1015627).
                Categories
                Research Article
                Biology and Life Sciences
                Anatomy
                Brain
                Prefrontal Cortex
                Medicine and Health Sciences
                Anatomy
                Brain
                Prefrontal Cortex
                Biology and Life Sciences
                Psychology
                Emotions
                Social Sciences
                Psychology
                Emotions
                Medicine and Health Sciences
                Hematology
                Hemodynamics
                Social Sciences
                Sociology
                Communications
                Marketing
                Physical Sciences
                Mathematics
                Probability Theory
                Probability Distribution
                Skewness
                Research and Analysis Methods
                Imaging Techniques
                Neuroimaging
                Biology and Life Sciences
                Neuroscience
                Neuroimaging
                Engineering and Technology
                Signal Processing
                Peak Values
                Biology and Life Sciences
                Neuroscience
                Brain Mapping
                Functional Magnetic Resonance Imaging
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Functional Magnetic Resonance Imaging
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Functional Magnetic Resonance Imaging
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Functional Magnetic Resonance Imaging
                Research and Analysis Methods
                Imaging Techniques
                Neuroimaging
                Functional Magnetic Resonance Imaging
                Biology and Life Sciences
                Neuroscience
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                Functional Magnetic Resonance Imaging
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                All relevant data are within the manuscript and its Supporting Information files.

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