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

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      1 , 2 , 3 , 1 , 3 , *
      PLoS ONE
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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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          Toward integrating feature selection algorithms for classification and clustering

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            Comparison of linear, nonlinear, and feature selection methods for EEG signal classification.

            The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.
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              Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI.

              Functional near-infrared spectroscopy (fNIRS) is an optical imaging method that can be used for a brain-computer interface (BCI). In the present study, we concurrently measure and discriminate fNIRS signals evoked by three different mental activities, that is, mental arithmetic (MA), right-hand motor imagery (RI), and left-hand motor imagery (LI). Ten healthy subjects were asked to perform the MA, RI, and LI during a 10s task period. Using a continuous-wave NIRS system, signals were acquired concurrently from the prefrontal and the primary motor cortices. Multiclass linear discriminant analysis was utilized to classify MA vs. RI vs. LI with an average classification accuracy of 75.6% across the ten subjects, for a 2-7s time window during the a 10s task period. These results demonstrate the feasibility of implementing a three-class fNIRS-BCI using three different intentionally-generated cognitive tasks as inputs. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
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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
                : e0208843
                Affiliations
                [1 ] School of Mechanical Engineering, Pusan National University, Geumjeong-gu, Busan, Republic of Korea
                [2 ] School of Life Science and Technology, University of Electronic Science and Technology of China, West Hi-Tech Zone, Chengdu, Sichuan, P. R. China
                [3 ] Department of Cogno-Mechatronics Engineering, Pusan National University, Geumjeong-gu, Busan, Republic of Korea
                Tohoku University, JAPAN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-8528-4457
                Article
                PONE-D-18-24262
                10.1371/journal.pone.0208843
                6289445
                30533055
                228c87d5-3cfd-4f50-966a-84f4ba4064a9
                © 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.

                History
                : 16 August 2018
                : 25 November 2018
                Page count
                Figures: 8, Tables: 6, Pages: 19
                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
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                Functional Magnetic Resonance Imaging
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