Electroencephalographic, Heart Rate, and Galvanic Skin Response Assessment for an Advertising Perception Study: Application to Antismoking Public Service Announcements

Cartocci, Giulia; Caratù, Myriam; Modica, Enrica; Maglione, Anton Giulio; Rossi, Dario; Cherubino, Patrizia; Babiloni, Fabio

doi:10.3791/55872

Cited by 54 publications

(65 citation statements)

References 76 publications

(106 reference statements)

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“…Based on our literature review, we identified four autonomic EEG indices that have been utilized for evaluating the reactions of the people in marketing stimuli: (1) the approach-withdrawal (AW) motivation index; (2) effort index; (3) choice index; and (4) valence. Such indices assist marketers in understanding customer responses to products [30,31].…”

Section: Eeg Indicesmentioning

confidence: 99%

“…Positive AW values correspond to positive motivation (approach behaviors), expressed in terms of the higher activation of the left frontal cortex. In contrast, negative AW values correspond to negative motivation (avoidance behaviors), expressed in terms of the higher activation of the left frontal cortex [29][30][31][32].…”

Section: Aw Indexmentioning

confidence: 99%

“…Numerous researchers have demonstrated the reliability and dependability of the frontal alpha asymmetry as an effective marker in the emotion and neuromarketing research [29][30][31][32][33][34]. Touchette [29] calculated the frontal alpha asymmetry scores by considering the difference between the right and left power spectral densities divided by their sum, as obtained using electrodes F4 and F3.…”

Section: Aw Indexmentioning

confidence: 99%

“…This index acts as a sign of cognitive processing that results from mental fatigue [33], and it has been investigated extensively in neuromarketing research [3,24,28,33,35]. This factor demonstrates the importance of positive and negative emotional processing for the creation of the steady memory traces during advertising [30].…”

Section: Effort Indexmentioning

confidence: 99%

“…We classified these predictive features based on the EEG signal types: (1) rhythms; and (2) transient activities. [17] Viewing brands and products ERP P300 BCI [11] Watching TV ads EEG Alpha (for emotional state) EMG, BCI and GSR [36] Viewing brands and images ERP N200 and P300 BCI [37] Watching TV ads EEG Alpha band frontal asymmetry BCI [35] Watching TV ads EEG Theta and gamma Heart rate, BCI and GSR [28] Watching TV ads EEG Asymmetrical increase in theta and alpha in PSD BCI [38] Viewing brand names ERP N400 BCI and EOG for eye movement [18] Viewing products and prices ERP FN400, LPC, and P200 BCI [39] Viewing products EEG Alpha, beta, theta, gamma, and delta BCI, Eye tracking [40] Viewing products ERP P300 BCI [41] Watching TV ads EEG Theta and alpha Heart rate, BCI and GSR [3] Viewing products ERSP and ERP Theta, N200, and FRN BCI [24] Watching ads (movie trailers) EEG (64 electrodes) Beta and gamma oscillations BCI and EOG for eye movement (2 electrode) [20] Watching TV ads dense-array EEG Three epochs: 200-350, 350-500, and 500-800 BCI [42] Viewing brand names ERP LPP BCI [43] Viewing product images ERP N200, LPP, and PSW BCI [30] Watching ad videos EEG Theta and alpha Heart rate, BCI and GSR [22] Viewing product images EEG Delta, theta, alpha, beta, and gamma BCI [29] Viewing product images EEG Alpha BCI [44] Viewing ads of food products EEG Delta, theta, alpha, beta, and gamma BCI [32] Viewing products and prices EEG Theta BCI [31] Tasting drinks EEG Alpha BCI [33] Viewing and touching products EEG Alpha and theta BCI [27] Viewing product images ERSP, ERP Theta, beta and N200 BCI [45] Viewing tourism images, videos and words EEG Delta, theta, alpha, beta and gamma BCI and GSR…”

Section: Predictive Features For the Preferencesmentioning

confidence: 99%

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Deep Learning for EEG-Based Preference Classification in Neuromarketing

2020

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The traditional marketing methodologies (e.g., television commercials and newspaper advertisements) may be unsuccessful at selling products because they do not robustly stimulate the consumers to purchase a particular product. Such conventional marketing methods attempt to determine the attitude of the consumers toward a product, which may not represent the real behavior at the point of purchase. It is likely that the marketers misunderstand the consumer behavior because the predicted attitude does not always reflect the real purchasing behaviors of the consumers. This research study was aimed at bridging the gap between traditional market research, which relies on explicit consumer responses, and neuromarketing research, which reflects the implicit consumer responses. The EEG-based preference recognition in neuromarketing was extensively reviewed. Another gap in neuromarketing research is the lack of extensive data-mining approaches for the prediction and classification of the consumer preferences. Therefore, in this work, a deep-learning approach is adopted to detect the consumer preferences by using EEG signals from the DEAP dataset by considering the power spectral density and valence features. The results demonstrated that, although the proposed deep-learning exhibits a higher accuracy, recall, and precision compared with the k-nearest neighbor and support vector machine algorithms, random forest reaches similar results to deep learning on the same dataset.

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Section: Eeg Indicesmentioning

confidence: 99%

Section: Aw Indexmentioning

confidence: 99%

Section: Aw Indexmentioning

confidence: 99%

Section: Effort Indexmentioning

confidence: 99%

Section: Predictive Features For the Preferencesmentioning

confidence: 99%

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Deep Learning for EEG-Based Preference Classification in Neuromarketing

2020

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Measuring Neurophysiological Signals, Fixations and Self-report Data for Product Placement Effectiveness Assessment in Music Videos

Martinez-Levy

Cartocci

Modica

et al. 2019

Experimental and Quantitative Methods in Contemporary Economics

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Product placement is a marketing technique that, by inserting products into a narrative structure, constitutes a likely effective tool to increase the visibility and notoriety of a brand. For years, the opportunities for product placement in music videos were limited.Recently there has been a growth of interest for this tool/advertising modality since the digital community allowed the possibility to move videos from television to the Internet. The scope of this study is to investigate the effectiveness of product placement in music videos. An electroencephalographic (EEG) index called mental effort (ME) has been analyzed, in addition to the emotional index (EI), calculated by the combination of Galvanic Skin Response (GSR) and Heart Rate (HR) signals. Self-report responses have also been collected through an online questionnaire and interviews, since one experimental question was to investigate whether viewing a video containing a commercial product could influence the declared recall of the product inserted in it and the spontaneous recall of the video itself. Furthermore, fixations related to the product inserted in videos have been obtained by the eye tracking technique (ET).Higher values of the ME (p=0,016) and EI (p = 0,033) have been found for videos with product placement in comparison to videos without it. In addition, results show that the number of fixations affects the recall of the showed products (p<0,001). These findings highlight that using product placement in famous singers' music videos is an effective technique for prompting product recall and how it helps to focus the visual attention on them.

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