A Mobile Application for Estimating Emotional Valence Using a Single-Channel EEG Device

Ogino, Mikito; Mitsukura, Yasue

doi:10.23919/sice.2018.8492583

Cited by 10 publications

(13 citation statements)

References 14 publications

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“…With the help of NeroSkyLab, the provided scientific research tool, data viewing and analysis can be conducted easily by non-engineer population. In 2015, Soria Morillo et al and Ogino and Mitsukura in 2018 conducted Neuromarketing experiment with NeuroSky device and with the help of machine learning algorithm, their choice prediction accuracy was over 70% [40,68]. A 10-channel EEG device BrainAmp, from BrainProducts GmBh was used in the Neuromarketing experiment conducted by Cherubino et al [42].…”

Section: Neural Response Recording Techniquesmentioning

confidence: 99%

“…Another type of filter Savitzky-Golay is found in use by Yadava et al [18] for signal smoothing. For noise and artifact removal, the 4 th -order Butterworth filter was used in the studies of Ogino and Mitsukura [68] and Oon et al [55].…”

Section: Brain Signal Processing In Neuromarketingmentioning

confidence: 99%

“…Emotional valence recognition between excitement and boredom using EEG device and combining SVM, KNN, SVR, LR (Ogino and Mitsukura) [68] 72.4%…”

Section: %mentioning

confidence: 99%

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Technological advancements and opportunities in Neuromarketing: a systematic review

et al. 2020

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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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Section: Neural Response Recording Techniquesmentioning

confidence: 99%

Section: Brain Signal Processing In Neuromarketingmentioning

confidence: 99%

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Technological advancements and opportunities in Neuromarketing: a systematic review

et al. 2020

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“…From the resulting signal we extracted 13 statistical descriptors and used them as input features of a Support Vector Machines classifier: it resulted that it is possible to detect "High Valence" stimuli with a mean accuracy of 76.63% at cross validation (Supplementary Table 2). We compared our method that uses a single channel sEMG (ZygoTrace) with a recent work on valence classification using a single channel EEG device (Ogino and Mitsukura, 2018). It seems that methods perform similarly, with the single channel EEG valence predictor achieving 72.40% accuracy on binary classification of valence levels ( Table 9).…”

Section: Zygotrace and Previous Literature: Usage Of Zygomaticus For mentioning

confidence: 99%

A Minimal Setup for Spontaneous Smile Quantification Applicable for Valence Detection

Nascimben¹,

Ramsøy²

2020

Front. Psychol.

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Tracking emotional responses as they unfold has been one of the hallmarks of applied neuroscience and related disciplines, but recent studies suggest that automatic tracking of facial expressions have low validation. In this study, we focused on the direct measurement of facial muscles involved in expressions such as smiling. We used single-channel surface electromyography (sEMG) to evaluate the muscular activity from the Zygomaticus Major face muscle while participants watched music videos. Participants were then tasked with rating each video with regard to their thoughts and responses to each of them, including their judgment of emotional tone (“Valence”), personal preference (“Liking”) and rating of whether the video displayed strength and impression (“Dominance”). Using a minimal recording setup, we employed three ways to characterize muscular activity associated with spontaneous smiles. The total time spent smiling (ZygoNum), the average duration of smiles (ZygoLen), and instances of high valence (ZygoTrace). Our results demonstrate that Valence was the emotional dimension that was most related to the Zygomaticus activity. Here, the ZygoNum had higher discriminatory power than ZygoLen for Valence quantification. An additional investigation using fractal properties of sEMG time series confirmed previous studies of the Facial Action Coding System (FACS) documenting a smoother contraction of facial muscles for enjoyment smiles. Further analysis using ZygoTrace responses over time to the video events discerned “high valence” stimuli with a 76% accuracy. Additional validation of this approach came against previous findings on valence detection using features derived from a single channel EEG setup. We discuss these results in light of both the recent replication problems of facial expression measures, and in relation to the need for methods to reliably assess emotional responses in more challenging conditions, such as Virtual Reality, in which facial expressions are often covered by the equipment used.

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“…The article [14] propose a framework to recognize emotion based on human physiological signals using the pervasive wearable device. The authors of article [15] developed the model to estimate human emotions, especially valence by using single-channel EEG device. However, the advantages of clustering algorithm in improving emotional data collection efficiency and data quality are not fully exploited in the existing works.…”

Section: Related Workmentioning

confidence: 99%

Data collection scheme with minimum cost and location of emotional recognition edge devices

Jin

Qian

Chen

2019

Pers Ubiquit Comput

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This paper develops a real-time and reliable data collection system for big scale emotional recognition systems. Based on the data sample set collected in the initialization stage and by considering the dynamic migration of emotional recognition data, we design an adaptive Kth average device clustering algorithm for migration perception. We define a sub-modulus weight function, which minimizes the sum of the weights of the subsets covered by a cover to achieve high-precision device positioning. Combining the energy of the data collection devices and the energy of the wireless emotional device, we balance the data collection efficiency and energy consumption, and define a minimum access number problem based on energy and storage space constraints. By designing an approximate algorithm to solve the approximate minimum Steiner point problem, the continuous collection of emotional recognition data and the connectivity of data acquisition devices are guaranteed under the energy constraint of wireless devices. We validate the proposed algorithms through simulation experiments using different emotional recognition systems and different data scale. Furthermore, we analyze the proposed algorithms in terms of topology for devices classification, location accuracy, and data collection efficiency by comparing with the Bayesian classifier-based expectation maximization algorithm, the background difference-based moving target detection arithmetic averaging algorithm, and the Hungarian algorithm for solving the assignment problem.

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A Mobile Application for Estimating Emotional Valence Using a Single-Channel EEG Device

Cited by 10 publications

References 14 publications

Technological advancements and opportunities in Neuromarketing: a systematic review

Technological advancements and opportunities in Neuromarketing: a systematic review

A Minimal Setup for Spontaneous Smile Quantification Applicable for Valence Detection

Data collection scheme with minimum cost and location of emotional recognition edge devices

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