SURVEY OF SOFT BIOMETRIC TECHNIQUES FOR GENDER IDENTIFICATION

Biometrics checks can be productively utilized for localization of intrusion in access control systems by utilizing soft computing frameworks.Biometrics procedures can be to a great extent separated into conventional and soft biometrics. The study presents a survey of the available softtechniques and comparison for gender identification from biometric techniques

Gender Estimation from Fingerprints Using DWT and Entropy

Gender estimation from fingerprints have wide range of applications, especially in the field of forensics where identifying the gender of a criminal can reduce the list of suspects significantly. Although there have been quite a few research papers in the field of gender estimation from fingerprints most of those experiments used a lot of features but were only able to achieve poor classification results. That being the motivation behind the study we successfully proposed two different approaches for gender estimation from fingerprints and achieved high classification accuracy.;In this study we have developed two different approaches for gender estimation from fingerprints. The dataset used consists of 498 fingerprints of which 260 are male and 238 are female fingerprints. The first approach is based on wavelet analysis and uses features obtained from a six level discrete wavelet transform (DWT). Classification is performed using a decision stump classifier implemented in weka and was able to achieve a classification accuracy of 95.38% using the DWT approach. The second approach uses wavelet packet analysis and extracted the Shannon entropy and log-energy entropy from the coefficients of wavelet packet transform and provided a classification accuracy of 96.59% on the same dataset using decision stump classifier implemented in weka

Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis

<p>Abstract</p> <p>Background</p> <p>The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD). In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA).</p> <p>Method</p> <p>Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence.</p> <p>Results and Discussion</p> <p>The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA) approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD.</p> <p>Conclusion</p> <p>This KPCA-based method requires only a digital camera and a decorated corridor setup. The ease of use and installation of the current method provides clinicians and researchers a low cost solution to monitor the progression of and the treatment to PD. In summary, the proposed method provides an alternative to perform gait analysis for patients with PD.</p

Face recognition by means of advanced contributions in machine learning

Face recognition (FR) has been extensively studied, due to both scientific fundamental challenges and current and potential applications where human identification is needed. FR systems have the benefits of their non intrusiveness, low cost of equipments and no useragreement requirements when doing acquisition, among the most important ones. Nevertheless, despite the progress made in last years and the different solutions proposed, FR performance is not yet satisfactory when more demanding conditions are required (different viewpoints, blocked effects, illumination changes, strong lighting states, etc). Particularly, the effect of such non-controlled lighting conditions on face images leads to one of the strongest distortions in facial appearance. This dissertation addresses the problem of FR when dealing with less constrained illumination situations. In order to approach the problem, a new multi-session and multi-spectral face database has been acquired in visible, Near-infrared (NIR) and Thermal infrared (TIR) spectra, under different lighting conditions. A theoretical analysis using information theory to demonstrate the complementarities between different spectral bands have been firstly carried out. The optimal exploitation of the information provided by the set of multispectral images has been subsequently addressed by using multimodal matching score fusion techniques that efficiently synthesize complementary meaningful information among different spectra. Due to peculiarities in thermal images, a specific face segmentation algorithm has been required and developed. In the final proposed system, the Discrete Cosine Transform as dimensionality reduction tool and a fractional distance for matching were used, so that the cost in processing time and memory was significantly reduced. Prior to this classification task, a selection of the relevant frequency bands is proposed in order to optimize the overall system, based on identifying and maximizing independence relations by means of discriminability criteria. The system has been extensively evaluated on the multispectral face database specifically performed for our purpose. On this regard, a new visualization procedure has been suggested in order to combine different bands for establishing valid comparisons and giving statistical information about the significance of the results. This experimental framework has more easily enabled the improvement of robustness against training and testing illumination mismatch. Additionally, focusing problem in thermal spectrum has been also addressed, firstly, for the more general case of the thermal images (or thermograms), and then for the case of facialthermograms from both theoretical and practical point of view. In order to analyze the quality of such facial thermograms degraded by blurring, an appropriate algorithm has been successfully developed. Experimental results strongly support the proposed multispectral facial image fusion, achieving very high performance in several conditions. These results represent a new advance in providing a robust matching across changes in illumination, further inspiring highly accurate FR approaches in practical scenarios.El reconeixement facial (FR) ha estat àmpliament estudiat, degut tant als reptes fonamentals científics que suposa com a les aplicacions actuals i futures on requereix la identificació de les persones. Els sistemes de reconeixement facial tenen els avantatges de ser no intrusius,presentar un baix cost dels equips d’adquisició i no la no necessitat d’autorització per part de l’individu a l’hora de realitzar l'adquisició, entre les més importants. De totes maneres i malgrat els avenços aconseguits en els darrers anys i les diferents solucions proposades, el rendiment del FR encara no resulta satisfactori quan es requereixen condicions més exigents (diferents punts de vista, efectes de bloqueig, canvis en la il·luminació, condicions de llum extremes, etc.). Concretament, l'efecte d'aquestes variacions no controlades en les condicions d'il·luminació sobre les imatges facials condueix a una de les distorsions més accentuades sobre l'aparença facial. Aquesta tesi aborda el problema del FR en condicions d'il·luminació menys restringides. Per tal d'abordar el problema, hem adquirit una nova base de dades de cara multisessió i multiespectral en l'espectre infraroig visible, infraroig proper (NIR) i tèrmic (TIR), sota diferents condicions d'il·luminació. En primer lloc s'ha dut a terme una anàlisi teòrica utilitzant la teoria de la informació per demostrar la complementarietat entre les diferents bandes espectrals objecte d’estudi. L'òptim aprofitament de la informació proporcionada pel conjunt d'imatges multiespectrals s'ha abordat posteriorment mitjançant l'ús de tècniques de fusió de puntuació multimodals, capaces de sintetitzar de manera eficient el conjunt d’informació significativa complementària entre els diferents espectres. A causa de les característiques particulars de les imatges tèrmiques, s’ha requerit del desenvolupament d’un algorisme específic per la segmentació de les mateixes. En el sistema proposat final, s’ha utilitzat com a eina de reducció de la dimensionalitat de les imatges, la Transformada del Cosinus Discreta i una distància fraccional per realitzar les tasques de classificació de manera que el cost en temps de processament i de memòria es va reduir de forma significa. Prèviament a aquesta tasca de classificació, es proposa una selecció de les bandes de freqüències més rellevants, basat en la identificació i la maximització de les relacions d'independència per mitjà de criteris discriminabilitat, per tal d'optimitzar el conjunt del sistema. El sistema ha estat àmpliament avaluat sobre la base de dades de cara multiespectral, desenvolupada pel nostre propòsit. En aquest sentit s'ha suggerit l’ús d’un nou procediment de visualització per combinar diferents bandes per poder establir comparacions vàlides i donar informació estadística sobre el significat dels resultats. Aquest marc experimental ha permès més fàcilment la millora de la robustesa quan les condicions d’il·luminació eren diferents entre els processos d’entrament i test. De forma complementària, s’ha tractat la problemàtica de l’enfocament de les imatges en l'espectre tèrmic, en primer lloc, pel cas general de les imatges tèrmiques (o termogrames) i posteriorment pel cas concret dels termogrames facials, des dels punt de vista tant teòric com pràctic. En aquest sentit i per tal d'analitzar la qualitat d’aquests termogrames facials degradats per efectes de desenfocament, s'ha desenvolupat un últim algorisme. Els resultats experimentals recolzen fermament que la fusió d'imatges facials multiespectrals proposada assoleix un rendiment molt alt en diverses condicions d’il·luminació. Aquests resultats representen un nou avenç en l’aportació de solucions robustes quan es contemplen canvis en la il·luminació, i esperen poder inspirar a futures implementacions de sistemes de reconeixement facial precisos en escenaris no controlats.Postprint (published version

Evaluation of Facial Asymmetry Using Soft-Tissue Thickness for Forensic Purposes

Munoz, SRT (reprint author), Univ Talca, Dept Ciencias Basicas Biomed, Ave Lircay S-N, Talca, Chile. Munoz, SRT (Torres Munoz, Sebastian Rene); Galdames, IS (Suazo Galdames, Ivan)Facial reconstruction for forensic sculpture aims to reproduce the face of an individual for identification. This technique is based on the knowledge of the facial soft-tissue thickness, which differs in terms of sexual dimorphism. However, in terms of asymmetry, the real significance of the soft-tissue thickness on both sides of the face is not considered to make an approximation of the morphofacial characteristics of an individual. This study analyzed the facial tissue thickness of 32 adult Spanish corpses of both sexes in six bilateral cephalometric landmarks through the needle puncture technique, comparing the measurements of right and left sides. No significant differences were found when comparing the soft-tissue thickness on the right and left sides in the total sample (p <0.05), or when comparing the values in men and women (p<0.05). The facial morphology is created by internal and external forces exerted on the soft tissue and influenced by their evolutionary development in vivo, where asymmetry parameters have a genetic and muscular determination, which in normal individuals do not represent a significant difference in the process of reconstruction of forensic sculpture, and can reliably standardize the entire information of facial thickness to the right or left side of the face

Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising

[EN] The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In addition, and using an artificial neural network based on neuroscience metrics, the model classifies (82.9% of average accuracy) and estimate the number of online views (mean error of 0.199). The results highlight the validity of neuromarketing-based techniques for predicting the success of advertising responses. Practitioners can consider the proposed methodology at the design stages of advertising content, thus enhancing advertising effectiveness. The study pioneers the use of neurophysiological methods in predicting advertising success in a digital context. This is the first article that has examined whether these measures could actually be used for predicting views for advertising on YouTube.This work has been supported by the Heineken Endowed Chair in Neuromarketing at the Polytechnic University of Valencia in order to research and apply new technologies and neuroscience in communication, distribution and consumption fields.Guixeres Provinciale, J.; Bigné-Alcañiz, E.; Ausin-Azofra, JM.; Alcañiz Raya, ML.; Colomer, A.; Fuentes-Hurtado, FJ.; Naranjo Ornedo, V. (2017). Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising. Frontiers in Psychology. 8:1-14. https://doi.org/10.3389/fpsyg.2017.01808S1148Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C. M., & Suri, J. S. (2006). Heart rate variability: a review. Medical & Biological Engineering & Computing, 44(12), 1031-1051. doi:10.1007/s11517-006-0119-0Aftanas, L. I., Reva, N. V., Varlamov, A. A., Pavlov, S. V., & Makhnev, V. P. (2004). Analysis of Evoked EEG Synchronization and Desynchronization in Conditions of Emotional Activation in Humans: Temporal and Topographic Characteristics. Neuroscience and Behavioral Physiology, 34(8), 859-867. doi:10.1023/b:neab.0000038139.39812.ebAstolfi, L., De Vico Fallani, F., Cincotti, F., Mattia, D., Bianchi, L., Marciani, M. G., … Babiloni, F. (2008). Neural Basis for Brain Responses to TV Commercials: A High-Resolution EEG Study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 16(6), 522-531. doi:10.1109/tnsre.2008.2009784Astolfi, L., Fallani, F. D. V., Cincotti, F., Mattia, D., Bianchi, L., Marciani, M. G., … Babiloni, F. (2009). Brain activity during the memorization of visual scenes from TV commercials: An application of high resolution EEG and steady state somatosensory evoked potentials technologies. Journal of Physiology-Paris, 103(6), 333-341. doi:10.1016/j.jphysparis.2009.07.002Baack, D. W., Wilson, R. T., & Till, B. D. (2008). Creativity and Memory Effects: Recall, Recognition, and an Exploration of Nontraditional Media. Journal of Advertising, 37(4), 85-94. doi:10.2753/joa0091-3367370407Bellman, S., Murphy, J., Treleaven-Hassard, S., O’Farrell, J., Qiu, L., & Varan, D. (2013). Using Internet Behavior to Deliver Relevant Television Commercials. Journal of Interactive Marketing, 27(2), 130-140. doi:10.1016/j.intmar.2012.12.001Bigné, E. (2016). Frontiers in research in business: Will you be in? European Journal of Management and Business Economics, 25(3), 89-90. doi:10.1016/j.redeen.2016.09.001Bigné, E., Llinares, C., & Torrecilla, C. (2016). Elapsed time on first buying triggers brand choices within a category: A virtual reality-based study. Journal of Business Research, 69(4), 1423-1427. doi:10.1016/j.jbusres.2015.10.119Blanco-Velasco, M., Weng, B., & Barner, K. E. (2008). ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Computers in Biology and Medicine, 38(1), 1-13. doi:10.1016/j.compbiomed.2007.06.003Boksem, M. A. S., & Smidts, A. (2015). Brain Responses to Movie Trailers Predict Individual Preferences for Movies and Their Population-Wide Commercial Success. Journal of Marketing Research, 52(4), 482-492. doi:10.1509/jmr.13.0572Bradley, M. M., Houbova, P., Miccoli, L., Costa, V. D., & Lang, P. J. (2011). Scan patterns when viewing natural scenes: Emotion, complexity, and repetition. Psychophysiology, 48(11), 1544-1553. doi:10.1111/j.1469-8986.2011.01223.xCastiglioni, P., & Di Rienzo, M. (s. f.). On the evaluation of heart rate spectra: the Lomb periodogram. Computers in Cardiology 1996. doi:10.1109/cic.1996.542584Cha, M., Kwak, H., Rodriguez, P., Ahn, Y.-Y., & Moon, S. (2007). I tube, you tube, everybody tubes. Proceedings of the 7th ACM SIGCOMM conference on Internet measurement - IMC ’07. doi:10.1145/1298306.1298309Chen, L., Zhou, Y., & Chiu, D. M. (2014). A lifetime model of online video popularity. 2014 23rd International Conference on Computer Communication and Networks (ICCCN). doi:10.1109/icccn.2014.6911774Christoforou, C., Christou-Champi, S., Constantinidou, F., & Theodorou, M. (2015). From the eyes and the heart: a novel eye-gaze metric that predicts video preferences of a large audience. Frontiers in Psychology, 6. doi:10.3389/fpsyg.2015.00579Colomer Granero, A., Fuentes-Hurtado, F., Naranjo Ornedo, V., Guixeres Provinciale, J., Ausín, J. M., & Alcañiz Raya, M. (2016). A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents. Frontiers in Computational Neuroscience, 10. doi:10.3389/fncom.2016.00074Couwenberg, L. E., Boksem, M. A. S., Dietvorst, R. C., Worm, L., Verbeke, W. J. M. I., & Smidts, A. (2017). Neural responses to functional and experiential ad appeals: Explaining ad effectiveness. International Journal of Research in Marketing, 34(2), 355-366. doi:10.1016/j.ijresmar.2016.10.005Curry, B., & Moutinho, L. (1993). Neural Networks in Marketing: Modelling Consumer Responses to Advertising Stimuli. European Journal of Marketing, 27(7), 5-20. doi:10.1108/03090569310040325Daugherty, T., Hoffman, E., & Kennedy, K. (2016). Research in reverse: Ad testing using an inductive consumer neuroscience approach. Journal of Business Research, 69(8), 3168-3176. doi:10.1016/j.jbusres.2015.12.005Davidson, R. J. (2004). What does the prefrontal cortex «do» in affect: perspectives on frontal EEG asymmetry research. Biological Psychology, 67(1-2), 219-234. doi:10.1016/j.biopsycho.2004.03.008Deitz, G. D., Royne, M. B., Peasley, M. C., & Huang, J. «Coco». (2016). EEG-Based Measures versus Panel Ratings: Predicting Social-Media Based Behavioral Responses to Super Bowl Ads. Journal of Advertising Research, 56(2), 217. doi:10.2501/jar-2016-030Demarzo, M. M. P., Montero-Marin, J., Stein, P. K., Cebolla, A. s, Provinciale, J. G., & García-Campayo, J. (2014). Mindfulness may both moderate and mediate the effect of physical fitness on cardiovascular responses to stress: a speculative hypothesis. Frontiers in Physiology, 5. doi:10.3389/fphys.2014.00105Santos, R. D. O. J. dos, Oliveira, J. H. C. de, Rocha, J. B., & Giraldi, J. D. M. E. (2015). Eye Tracking in Neuromarketing: A Research Agenda for Marketing Studies. International Journal of Psychological Studies, 7(1). doi:10.5539/ijps.v7n1p32Elsen, M., Pieters, R., & Wedel, M. (2016). Thin Slice Impressions: How Advertising Evaluation Depends on Exposure Duration. Journal of Marketing Research, 53(4), 563-579. doi:10.1509/jmr.13.0398Feise, R. J. (2002). Do multiple outcome measures require p-value adjustment? BMC Medical Research Methodology, 2(1). doi:10.1186/1471-2288-2-8Fishman, M., Jacono, F. J., Park, S., Jamasebi, R., Thungtong, A., Loparo, K. A., & Dick, T. E. (2012). A method for analyzing temporal patterns of variability of a time series from Poincaré plots. Journal of Applied Physiology, 113(2), 297-306. doi:10.1152/japplphysiol.01377.2010Fjorback, L. O., Arendt, M., Ørnbøl, E., Fink, P., & Walach, H. (2011). Mindfulness-Based Stress Reduction and Mindfulness-Based Cognitive Therapy - a systematic review of randomized controlled trials. Acta Psychiatrica Scandinavica, 124(2), 102-119. doi:10.1111/j.1600-0447.2011.01704.xGao, J. F., Yang, Y., Lin, P., Wang, P., & Zheng, C. X. (2009). Automatic Removal of Eye-Movement and Blink Artifacts from EEG Signals. Brain Topography, 23(1), 105-114. doi:10.1007/s10548-009-0131-4Geisler, F. C. M., Vennewald, N., Kubiak, T., & Weber, H. (2010). The impact of heart rate variability on subjective well-being is mediated by emotion regulation. Personality and Individual Differences, 49(7), 723-728. doi:10.1016/j.paid.2010.06.015Goldberg, J. H., Stimson, M. J., Lewenstein, M., Scott, N., & Wichansky, A. M. (2002). Eye tracking in web search tasks. Proceedings of the symposium on Eye tracking research & applications - ETRA ’02. doi:10.1145/507072.507082Grandjean, D., Sander, D., & Scherer, K. R. (2008). Conscious emotional experience emerges as a function of multilevel, appraisal-driven response synchronization. Consciousness and Cognition, 17(2), 484-495. doi:10.1016/j.concog.2008.03.019Guerreiro, J., Rita, P., & Trigueiros, D. (2015). Attention, emotions and cause-related marketing effectiveness. European Journal of Marketing, 49(11/12), 1728-1750. doi:10.1108/ejm-09-2014-0543Ha, L. (2008). Online Advertising Research in Advertising Journals: A Review. Journal of Current Issues & Research in Advertising, 30(1), 31-48. doi:10.1080/10641734.2008.10505236Harmon-Jones, E., Gable, P. A., & Peterson, C. K. (2010). The role of asymmetric frontal cortical activity in emotion-related phenomena: A review and update. Biological Psychology, 84(3), 451-462. doi:10.1016/j.biopsycho.2009.08.010Holmqvist, K., Andrà, C., Lindström, P., Arzarello, F., Ferrara, F., Robutti, O., & Sabena, C. (2011). A method for quantifying focused versus overview behavior in AOI sequences. Behavior Research Methods, 43(4), 987-998. doi:10.3758/s13428-011-0104-xKent, R. J., & Allen, C. T. (1994). Competitive Interference Effects in Consumer Memory for Advertising: The Role of Brand Familiarity. Journal of Marketing, 58(3), 97. doi:10.2307/1252313Khushaba, R. N., Wise, C., Kodagoda, S., Louviere, J., Kahn, B. E., & Townsend, C. (2013). Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Systems with Applications, 40(9), 3803-3812. doi:10.1016/j.eswa.2012.12.095Kim, K., Hayes, J. L., Avant, J. A., & Reid, L. N. (2014). Trends in Advertising Research: A Longitudinal Analysis of Leading Advertising, Marketing, and Communication Journals, 1980 to 2010. Journal of Advertising, 43(3), 296-316. doi:10.1080/00913367.2013.857620Kopton, I. M., & Kenning, P. (2014). Near-infrared spectroscopy (NIRS) as a new tool for neuroeconomic research. Frontiers in Human Neuroscience, 8. doi:10.3389/fnhum.2014.00549Kühn, S., Strelow, E., & Gallinat, J. (2016). Multiple «buy buttons» in the brain: Forecasting chocolate sales at point-of-sale based on functional brain activation using fMRI. NeuroImage, 136, 122-128. doi:10.1016/j.neuroimage.2016.05.021Lang, A., Bolls, P., Potter, R. F., & Kawahara, K. (1999). The effects of production pacing and arousing content on the information processing of television messages. Journal of Broadcasting & Electronic Media, 43(4), 451-475. doi:10.1080/08838159909364504Lee, J., & Ahn, J.-H. (2012). Attention to Banner Ads and Their Effectiveness: An Eye-Tracking Approach. International Journal of Electronic Commerce, 17(1), 119-137. doi:10.2753/jec1086-4415170105McAlister, L., Srinivasan, R., Jindal, N., & Cannella, A. A. (2016). Advertising Effectiveness: The Moderating Effect of Firm Strategy. Journal of Marketing Research, 53(2), 207-224. doi:10.1509/jmr.13.0285McDuff, D., Kaliouby, R. E., Cohn, J. F., & Picard, R. W. (2015). Predicting Ad Liking and Purchase Intent: Large-Scale Analysis of Facial Responses to Ads. IEEE Transactions on Affective Computing, 6(3), 223-235. doi:10.1109/taffc.2014.2384198Mognon, A., Jovicich, J., Bruzzone, L., & Buiatti, M. (2011). ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology, 48(2), 229-240. doi:10.1111/j.1469-8986.2010.01061.xBabiloni, F. (2012). Consumer Nueroscience: A New Area of Study for Biomedical Engineers. IEEE Pulse, 3(3), 21-23. doi:10.1109/mpul.2012.2189166Mould, D., Mandryk, R. L., & Li, H. (2012). Emotional response and visual attention to non-photorealistic images. Computers & Graphics, 36(6), 658-672. doi:10.1016/j.cag.2012.03.039Cartocci, G., Caratù, M., Modica, E., Maglione, A. G., Rossi, D., Cherubino, P., & Babiloni, F. (2017). Electroencephalographic, Heart Rate, and Galvanic Skin Response Assessment for an Advertising Perception Study: Application to Antismoking Public Service Announcements. Journal of Visualized Experiments, (126). doi:10.3791/55872Pan, J., & Tompkins, W. J. (1985). A Real-Time QRS Detection Algorithm. IEEE Transactions on Biomedical Engineering, BME-32(3), 230-236. doi:10.1109/tbme.1985.325532Pieters, R., Warlop, L., & Wedel, M. (2002). Breaking Through the Clutter: Benefits of Advertisement Originality and Familiarity for Brand Attention and Memory. Management Science, 48(6), 765-781. doi:10.1287/mnsc.48.6.765.192Piskorski, J., & Guzik, P. (2007). Geometry of the Poincaré plot ofRRintervals and its asymmetry in healthy adults. Physiological Measurement, 28(3), 287-300. doi:10.1088/0967-3334/28/3/005Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039-H2049. doi:10.1152/ajpheart.2000.278.6.h2039Ruanguttamanun, C. (2014). Neuromarketing: I Put Myself into a fMRI Scanner and Realized that I love Louis Vuitton Ads. Procedia - Social and Behavioral Sciences, 148, 211-218. doi:10.1016/j.sbspro.2014.07.036Shehu, E., Bijmolt, T. H. A., & Clement, M. (2016). Effects of Likeability Dynamics on Consumers’ Intention to Share Online Video Advertisements. Journal of Interactive Marketing, 35, 27-43. doi:10.1016/j.intmar.2016.01.001Smith, A. N., Fischer, E., & Yongjian, C. (2012). How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter? Journal of Interactive Marketing, 26(2), 102-113. doi:10.1016/j.intmar.2012.01.002Strach, P., Zuber, K., Fowler, E. F., Ridout, T. N., & Searles, K. (2015). In a Different Voice? Explaining the Use of Men and Women as Voice-Over Announcers in Political Advertising. Political Communication, 32(2), 183-205. doi:10.1080/10584609.2014.914614Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, A. J., & Schwartz, P. J. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. European Heart Journal, 17(3), 354-381. doi:10.1093/oxfordjournals.eurheartj.a014868Tomkovick, C., Yelkur, R., & Christians, L. (2001). The USA’s biggest marketing event keeps getting bigger: an in-depth look at Super Bowl advertising in the 1990s. Journal of Marketing Communications, 7(2), 89-108. doi:10.1080/13527260121725Vakratsas, D., & Ambler, T. (1999). How Advertising Works: What Do We Really Know? Journal of Marketing, 63(1), 26. doi:10.2307/1251999Valenza, G., Allegrini, P., Lanatà, A., & Scilingo, E. P. (2012). Dominant Lyapunov exponent and approximate entropy in heart rate variability during emotional visual elicitation. Frontiers in Neuroengineering, 5. doi:10.3389/fneng.2012.00003Valenza, G., Citi, L., Lanatá, A., Scilingo, E. P., & Barbieri, R. (2014). Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics. Scientific Reports, 4(1). doi:10.1038/srep04998Varan, D., Lang, A., Barwise, P., Weber, R., & Bellman, S. (2015). How Reliable Are Neuromarketers’ Measures of Advertising Effectiveness? Journal of Advertising Research, 55(2), 176-191. doi:10.2501/jar-55-2-176-191Vecchiato, G., Astolfi, L., Tabarrini, A., Salinari, S., Mattia, D., Cincotti, F., … Babiloni, F. (2010). EEG Analysis of the Brain Activity during the Observation of Commercial, Political, or Public Service Announcements. Computational Intelligence and Neuroscience, 2010, 1-7. doi:10.1155/2010/985867Vecchiato, G., Maglione, A. G., Cherubino, P., Wasikowska, B., Wawrzyniak, A., Latuszynska, A., … Babiloni, F. (2014). Neurophysiological Tools to Investigate Consumer’s Gender Differences during the Observation of TV Commercials. Computational and Mathematical Methods in Medicine, 2014, 1-12. doi:10.1155/2014/912981Vecchiato, G., Susac, A., Margeti, S., De Vico Fallani, F., Maglione, A. G., Supek, S., … Babiloni, F. (2012). High-Resolution EEG Analysis of Power Spectral Density Maps and Coherence Networks in a Proportional Reasoning Task. Brain Topography, 26(2), 303-314. doi:10.1007/s10548-012-0259-5Vecchiato, G., Toppi, J., Astolfi, L., De Vico Fallani, F., Cincotti, F., Mattia, D., … Babiloni, F. (2011). Spectral EEG frontal asymmetries correlate with the experienced pleasantness of TV commercial advertisements. Medical & Biological Engineering & Computing, 49(5), 579-583. doi:10.1007/s11517-011-0747-xVenkatraman, V., Dimoka, A., Pavlou, P. A., Vo, K., Hampton, W., Bollinger, B., … Winer, R. S. (2015). Predicting Advertising success beyond Traditional Measures: New Insights from Neurophysiological Methods and Market Response Modeling. Journal of Marketing Research, 52(4), 436-452. doi:10.1509/jmr.13.0593Wackermann, J., Lehmann, D., Michel, C. M., & Strik, W. K. (1993). Adaptive segmentation of spontaneous EEG map series into spatially defined microstates. International Journal of Psychophysiology, 14(3), 269-283. doi:10.1016/0167-8760(93)90041-mWedel, M., & Kannan, P. K. (2016). Marketing Analytics for Data-Rich Environments. Journal of Marketing, 80(6), 97-121. doi:10.1509/jm.15.0413Werkle-Bergner, M., Müller, V., Li, S.-C., & Lindenberger, U. (2006). Cortical EEG correlates of successful memory encoding: Implications for lifespan comparisons. Neuroscience & Biobehavioral Reviews, 30(6), 839-854. doi:10.1016/j.neubiorev.2006.06.009West, P. M., Brockett, P. L., & Golden, L. L. (1997). A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice. Marketing Science, 16(4), 370-391. doi:10.1287/mksc.16.4.370Zhou, R., Khemmarat, S., Gao, L., Wan, J., & Zhang, J. (2016). How YouTube videos are discovered and its impact on video views. Multimedia Tools and Applications, 75(10), 6035-6058. doi:10.1007/s11042-015-3206-